DIGITAL IMAGE WATERMARKING Thesis submitted in the fulfillment of the Degree of Doctor of Philosophy by VIKAS SAXENA Department of Computer Science and Engineering JAYPEE INSTITUE OF INFORMATION TECHNOLOGY UNIVERSITY A-10, SECTOR-62, NOIDA, INDIA October, 2008
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DIGITAL IMAGE WATERMARKING
Thesis submitted in the fulfillment of the Degree of
Doctor of Philosophy
by
VIKAS SAXENA
Department of Computer Science and Engineering JAYPEE INSTITUE OF INFORMATION TECHNOLOGY UNIVERSITY
A-10, SECTOR-62, NOIDA, INDIA October, 2008
© JAYPEE INSTITUE OF INFORMATION TECHNOLOGY UNIVERSITY, NOIDA, INDIA October, 2008 ALL RIGHT RESERVED
SCHOLAR’S CERTIFICATE
This is to certify that the work reported in the Ph.D. thesis entitled “Digital Image
Watermarking” submitted at Jaypee Institute of Information Technology University,
Noida, India is an authentic record of my work carried out under the supervision of
Prof. J.P.Gupta. I have not submitted this work elsewhere for any other degree or diploma.
(Vikas Saxena)
Department of Computer Science and Engineering
Jaypee Institute of Information Technology University, Noida, India
October 10, 2008
SUPERVISOR’S CERTIFICATE
This is to certify that the work reported in the Ph.D. thesis entitled “Digital Image
Watermarking” submitted by Vikas Saxena at Jaypee Institute of Information
Technology University, Noida, India is a bonafide record of his original work carried out
under my supervision. This work has not been submitted elsewhere for any other degree or
diploma.
(Prof. J. P. Gupta)
Vice Chancellor
Jaypee Institute of Information Technology University, Noida, India
October 10, 2008
i
TABLE OF CONTENTS
PageNo.
ABSTRACT vii
ACKNOWLEDGEMENT ix
LIST OF ACCRONYMS xi
LIST OF SYMBOLS xiii
LIST OF FIGURES xv
LIST OF TABLES xix
CHAPTER-1
INTRODUCTION 1
1. 1 DATA HIDING BACKGROUND 3 1.1.1 STEGANOGRAPHY VS. WATERMARKING 5 1.1.2 CRYPTOGRAPHY VS. WATERMARKING 5 1.1.3 DIGITAL SIGNATURE VS. WATERMARKING 6
1. 2 APPLICATION AREAS OF DIGITAL WATERMARKING 7 1.2.1 COPYRIGHT PROTECTION 7 1.2.2 COPY PROTECTION 7 1.2.3 TEMPER DETECTION 8 1.2.4 BROADCAST MONITORING 8 1.2.5 FINGERPRINTING 9 1.2.6 ANNOTATION APPLICATIONS 9
1. 3 CHARACTERISTICS OF WATERMARKING SCHEMES 10 1. 4 TYPES OF DIGITAL WATERMARKS 11 1. 5 STRUCTURE OF THE THESIS 15
ii
CHAPTER-2
IMAGE WATERMARKING LITERATURE SURVEY 17
2.1 SPATIAL DOMAIN BASED WATERMARKING SCHEMES 18 2.1.1 LSB BASED SCHEMES 18 2.1.2 PATCH WORK BASED SCHEME 18 2.1.3 CORRELATION BASED WATERMARKING SCHEMES 19
2.1.3.1 CORRELATION BASED SCHEMES WITH 1 PN SEQUENCE 19 2.1.3.2 CORRELATION-BASED IMAGE WATERMARKING SCHEMES
WITH 2PN SEQUENCES 19
2.1.3.3 IMAGE WATERMARKING USING PRE-FILTERING 20 2.1.4 CDMA BASED IMAGE WATERMARKING SCHEME 20 2.1.5 OTHER SPATIAL DOMAIN BASED WATERMARKING SCHEMES 21
2.2 TRANSFORMED DOMAIN BASED SCHEMES 22 2.2.1 DFT BASED WATERMARKING SCHEMES 22 2.2.2 DCT BASED WATERMARKING SCHEMES 24
2.2.2.1 THE MIDDLE-BAND COEFFICIENT EXCHANGE SCHEME 26 2.2.2.2 DCT-CDMA BASED IMAGE WATERMARKING 28
2.2.3 DWT BASED WATERMARKING SCHEMES 29 2.2.3.1 CDMA-DWT BASED WATERMARKING SCHEME 30 2.2.3.2 DWT BASED BLIND WATERMARK DETECTION 31 2.2.3.3 DWT BASED NON-BLIND WATERMARK DETECTION 32
2.3 RECENT METHODOLOGIES 33 2.4 PROBLEM STATEMENT FORMULATION 38
2.4.1 JUSTIFICATIONS OF THE PROBLEM STATEMENT CHOSEN 40
CHPATER-3
PRELIMINARIES 45
3.1 IMAGE ENCODING STANDARDS 45 3.1.1 JPEG ENCODING 45
iii
3.1.2 JPEG2000 ENCODING 53 3.2 IMAGE QUALITY MEASURES 56
3.2.1 PEAK SIGNAL TO NOISE RATIO 56 3.2.2 CORRELATION COEFFICIENT 57
3.3 TEST DATA 58
CHAPTER-4
WATERMARKING OF GRAY IMAGES 61
4.1 INTRODUCTION 61 4.2 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEMES
AGAINST JPEG COMPRESSION 62
4.3 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEME
AGAINST HISTOGRAM EQUALIZATION ATTACK 64
4.4 DEVISING A COLLUSION ATTACK RESISTANT WATERMARKING
SCHEME FOR IMAGES USING DCT 68
4.4.1 G, THE POLICY GENERATOR ALGORITHM 72 4.4.2 E, THE WATERMARK EMBEDDING ALGORITHM 72 4.4.3 D, THE WATERMARK DETECTION ALGORITHM 74 4.4.4 PERFORMANCE OF THE PROPOSED SCHEME 76
4.4.4.1 PERFORMANCE AGAINST JPEG COMPRESSION 76 4.4.4.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS 77 4.4.4.3 COMPARATIVE STUDY WITH OTHER MECHANISMS 77
4.5 CONCLUSION 79
CHAPTER-5
WATERMARKING of COLOR IMAGES 81
5.1 INTRODUCTION 81 5.2 PERFORMANCE ANALYSIS OF COLOR CHANNEL FOR DCT BASED
IMAGE WATERMARKING SCHEME 81
5.3 DEVISING AN ICAR WATERMARKING SCHEME FOR COLORED BMP
IMAGES 85
iv
5.3.1 G, THE POLICY GENERATOR ALGORITHM 86 5.3.2 COLOR CHANNEL SELECTION 87 5.3.3 E, THE WATERMARK EMBEDDING ALGORITHM 87 5.3.4 D, THE WATERMARK DETECTION ALGORITHM 88 5.3.5 PERFORMANCE OF THE PROPOSED SCHEME 90
5.3.5.1 PERFORMANCE AGAINST JPEG COMPRESSION 91 5.3.5.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS 92 5.3.5.3 COMPARATIVE STUDY RESULTS WITH OTHER SCHEMES 93
5.4 CONCLUSION 96
CHAPTER-6
WATERMARKING OF JPEG IMAGES 97
6.1 INTRODUCTION 97 6.2 DEVELOPMG AN ICAR WATERMARKING ALGORITHM FOR JPEG
IMAGES 97
6.2.1 G, THE POLICY GENERATOR ALGORITHM 99 6.2.1.1 COLOR CHANNEL SELECTION 100
6.2.2 E, THE WATERMARK EMBEDDING ALGORITHM 100 6.2.3 D, THE WATERMARK DETECTION ALGORITHM 102 6.2.4 PERFORMANCE OF THE PROPOSED SCHEME 104
6.2.4.1 COLOR CHANNEL SELECTION AND PERFORMANCE AGAINST
JPEG COMPRESSION 105
6.2.4.2 PERFORMANCE AGAINST IMAGE MANIPULATIONS 106 6.2.4.3 COMPARATIVE STUDY WITH SIMILAR, STATE-OF-THE-ART
SCHEMES
108 6.3 A DWT BASED WATERMARKING SCHEME FOR JPEG IMAGES 111
6.3.1 EXPLORATION OF DWT DOMAIN 112 6.3.1.1 ISSUES IN USING DWT 112
6.3.2 BACKGROUND OF THE PROPOSED SCHEME 114 6.3.3 DUAL WATERMARKING 115
v
6.3.4 THE DWT BASED WATERMARKING 115 6.3.4.1 P, THE POLICY 116 6.3.4.2 G, THE POLICY GENERATOR ALGORITHM 116 6.3.4.3 E, THE WATERMARK EMBEDDING ALGORITHM 118 6.3.4.4 D, THE WATERMARK DETECTION ALGORITHM 120
6.3.5 THE DCT BASED WATERMARKING 121 6.3.6 RESULTS 121
6.3.6.1 THE VALUE OF “T” 122 6.3.6.2 PERFORMANCE AGAINST JPEG COMPRESSION 126 6.3.6.3 PERFORMANCE AGAINST COMMON ATTACKS AND IMAGE
MANIPULATIONS 127
6.3.6.4 COMPARATIVE STUDY WITH DCT BASED SCHEMES 127 6.3.6.5 COMPARATIVE STUDY WITH DWT BASED SCHEMES 129
6.4 CONCLUSION 130
CHAPTER-7
RESULTS AND CONCLUSION 131
7.1 SUMMARY 131 7.2 MAIN CONTRIBUTIONS AND HIGHLIGHTS OF THE RESULTS 131 7.3 FUTURE WORK 132
REFERENCES 135
LIST OF AUTHOR’S PUBLICATION 147
SYNOPSIS
vi
vii
ABSTRACT
Watermarking has been invoked as a tool for the protection of Intellectual Property Rights
(IPR) of multimedia contents. Because of their digital nature, multimedia documents can be
duplicated, modified, transformed, and diffused very easily. In this context, it is important to
develop a system for copyright protection, protection against duplication, and authentication
of contents. For this, a watermark is embedded into the digital data in such a way that it is
indissolubly tied to the data itself. Later on, such watermark can be extracted to prove
ownership to trace the dissemination of the marked work through the network, or simply to
inform users about the identity of the rights-holder or about the allowed use of data.
This thesis deals the developing the watermarking schemes for digital images stored in both,
spatial and transformed domain. In this thesis we mainly focus on the Discrete Cosine
Transform (DCT) based development. To prove its commercial usability, we take special
care so that at least one attack, having huge financial implications, can be sustained due to
the in-built capacity of the watermarking scheme. Apart from this, since JPEG is the most
commonly used image format over WWW, we pay special attention to robustness against
JPEG compression attack.
Apart from developing watermarking schemes, we also discuss the selection of color channel
to be used to carry the watermark data based on the attack that may occur most commonly on
the watermarked images. We propose to increase the robustness against some attacks by pre-
processing the images. In this thesis, we also present a correlation between the performance
of the watermarking scheme against some attacks and the original image characteristics. All
presented watermarking schemes are robust against common image manipulations and
attacks.
viii
ix
ACKNOWLEDGEMENT
I am greatly indebted to my supervisor Prof. J. P. Gupta for his valuable technical guidance
and moral support through out this work. Without his support this thesis would have not been
completed.
I would also like to thank to Prof S.L Maskara, Prof Sanjay Goel and faculty members of the
department who always enlightened me by sharing their research experiences to accomplish
the quality work.
My mother provided me all support I needed to complete this thesis and other family
members specially my wife also helped me a lot in getting me this far.
Vikas Saxena
Department of Computer Science Engineering and Information Technology
Jaypee Institute of Information Technoogy University
Noida, India
x
xi
LIST OF ACCRONYMS CC Correlation Coefficient
CDMA Code Division Multiple Access
DCT Discrete Cosine Transform
DFT Discrete Fourier Transform
DWT Discrete Wavelet Transform
EBCOT Embedded Block Coding with Optimized Truncation
EZW Embedded Zero-tree Wavelet
FFT Fast Fourier Transform
HH High-High Band of DWT
HL High-Low Band of DWT
HVS Human Visual System
ICAR Inherently Collusion Attack Resistant
IPR Intellectual Property Right
JND Just Noticeable Distortion
JPEG Joint Photographic Expert Group
LH Low-High Band of DWT
LL Low-Low Band of DWT
LSB Least Significant Bit
MBCE Middle Band Coefficient Exchange
MSE Mean Square Error
PN Pseudo-random noise
PSNR Peak Signal to Noise ration
PSW Perceptually Shaped Watermarking
REL Run Length Encoding
RGB Red Green Blue
SPIHT Set Partitioning In Hierarchical Trees
SVD Singular Value Decomposition
VQ Vector Quantization
xii
xiii
LIST OF SYMBOLS
D Watermark detection algorithm
E Watermark embedding algorithm
FH High frequency region in an 8 x 8 DCT
FL Low frequency region in an 8 x 8 DCT
FM Middle frequency region in an 8 x 8 DCT
G Policy generator algorithm
K Watermark strength parameter
P Policy
Pi An instance of a policy
Q JPEG quantization factor
S Watermark logo converted into string of ‘0’s and ‘1’s
Sr A single bit of S
T Watermark strength parameter
W Watermark logo
Wi A single bit of the watermark data
X Original cover image
Xi An instance of the cover image
xiv
xv
LIST OF FIGURES
Figure No.
Caption Page No.
1.1 Watermark on the bank currency note 4
1.2 Various classifications of watermarking 12
1.3 Image watermark embedding scheme 13
1.4 Image watermark detection scheme 13
2.1 FIR Edge Enhancement Pre-Filter 20
2.2 A General Frequency domain based watermarking model as presented
by Cox
23
2.3 Frequency regions in 8 x 8 DCT 27
2.4 JPEG Quantization matrix 28
2.5 1-Scale and 2-Scale 2-Dimensional Discrete Wavelet Transform 30
2.6 The Targeted types of to be developed watermarking schemes 43
3.1 JPEG Compression Scheme 45
3.2 An example sub image 46
3.3 Example sub image after subtracting 128 from each pixel 47
3.4 DCT of sub image shown in Figure 3.3 47
3.5 JPEG Quantization matrix 49
3.6 DCT values after quantization 49
3.7 JPEG Decompression Scheme 51
3.8 DCT values regenerated in decompression 51
3.9 (a) Sub image pixel values (still shifted down by 128) 51
3.9 (b) Decompressed sub image pixel values 52
3.10 Error matrix for example sub image 52
3.11 Test images of Lena, Mandrill, Pepper and Barbara (Gray) 58
3.12 Test images of Lena, Mandrill, Pepper and Goldhill (Colored) 59
3.13 Watermark logo used in the proposed schemes
59
xvi
4.1 (a) Extracted watermark logos from test images of Lena, Mandrill and
Pepper by applying DCT based scheme
62
4.1 (b) Extracted watermark logos from test images of Lena, Mandrill and
Pepper by applying DWT based scheme
62
4.2 (a) Extracted watermark logos from test images of Lena, Mandrill,
Pepper and Barbara by applying DCT based scheme
66
4.2 (b) Extracted watermark logos from test images of Lena, Mandrill,
Pepper and Barbara by applying DWT based scheme
66
4.3 Extracted logos from “original image” (left) and “transformed image”
(right) of Lena, Mandrill, Pepper and Barbara’s (Top to Bottom)
histogram equalized images (By applying DCT based scheme)
68
4.4 Swapping of 4 pairs to hide “0” or “1” in conjunction with low
frequency values
71
4.5 Extracted watermark logos after JPEG compression at Q = 20 from
watermarked Lena, Mandrill and Pepper images
77
4.6 Extracted watermark logos from Lena’s image after Horizontal
flipped, scaled, brightness /contrast adjusted and Noising (Left to
Right, Top to bottom)
78
4.7 Percentage decrease in quality of extracted watermark with respect to
JPEG quality factor
79
5.1 Recovered watermarks for Lena.bmp after jpeg attack at Q = 40 82
5.2 Watermarked test images keeping T = 150 91
5.3 Extracted watermark from watermarked Lena, Mandrill and Pepper
images respectively at T = 150
91
5.4 Recovered logos from attacked images 94
5.5 Extracted logos using proposed scheme from highly compressed
watermarked test images
95
6.1 Watermarked test images generated by keeping T = 150 105
6.2 Extracted watermark logos from watermarked Lena, Mandrill, Pepper
and Goldhill test images respectively at T = 150
105
xvii
6.3 Goldhill test image after hiding the watermark logo and the recovered
logo at T = 100
105
6.4 Extracted logos from attacked watermarked images 109
6.5 Comparison of correlation coefficients at Q = 10 110
6.6 Comparison of correlation coefficients at Q = 5 111
6.7 2-D Haar DWT 113
6.8 An example of 2 consecutive DWT blocks 117
6.9 An example of 2 consecutive DWT blocks 117
6.10 Watermark embedding approach 120
6.11 The watermark logo 122
6.12 Graph of the values shown in Table 6.6 123
6.13 Extracted logos from Lena, Mandrill and Pepper’s test images 124
6.14 The extracted logos using DWT based method 125
6.15 The extracted logos using DCT based method 126
6.16 Extracted logos from highly compressed JPEG images 126
6.17 Extracted watermark logos after applying common attacks 128
xviii
xix
LIST OF TABLES
Table No.
Caption
Page No.
4.1 PSNR (in decibel) of extracted watermark logo from JPEG
compressed (Q = 20) watermarked image
65
4.2 PSNR of extracted logos from attacked test images 67
4.3 PSNR of extracted watermarks after JPEG compression 77
5.1 PSNR of Extracted watermark from JPEG compressed watermark test
images
82
5.2 PSNR of extracted watermark from attacked watermarked test images 84
5.3 PSNR of extracted watermark logos after JPEG compression 92
5.4 PSNR of extracted watermark logo from watermarked test images
after attacks
93
5.5 PSNR values of extracted logos from highly compressed watermarked
test images using various schemes
95
6.1 SD values of color channels for test images 106
6.2 PSNR and CC of extracted logo by using BLUE channel for all
images
107
6.3 PSNR and CC of extracted logo by using BLUE and GREEN
channels for images
108
6.4 CC of the extracted logos 108
6.5 PSNR of watermarked image and CC of extracted logo for various
values of T
122
6.6 Revised Table 6.5 122
6.7 CC of extracted logos from JPEG2000 attacked images 124
6.8 Decrement in the PSNR values after the application of DCT based
scheme
125
6.9 CC values of the extracted watermark logos recovered by both
recovery methods
125
xx
6.10 CC of extracted logo from highly compressed jpeg image using DCT
based recovery
126
6.11 CC of the extracted watermark logos 128
6.12 Comparison of CC of Extracted logos from JPEG compressed
(Q = 10) watermarked images
129
6.13 Comparison of CC of Extracted logos from JPEG compressed
(Q = 5) watermarked images
129
1
CHAPTER 1
INTRODUCTION
The growth of high speed computer networks and World Wide Web (WWW) have explored
means of new business, scientific, entertainment and social opportunities in the form of
electronic publishing and advertising, massaging, real-time information delivery, data
sharing, collaboration among computers, product ordering, transaction processing, digital
repositories and libraries, web newspapers and magazines, network video and audio, personal
communication and lots more. The cost effectiveness of selling softwares in the form of
digital images and video sequences by transmission over WWW is greatly enhanced due to
the improvement in technology.
We know that one of the biggest technological events of the last two decades was the
invasion of digital media in an entire range of everyday life aspects. Digital data can be
stored efficiently and with a very high quality, and it can be manipulated very easily using
computers. Furthermore, digital data can be transmitted in a fast and inexpensive way
through data communication networks without losing quality. Digital media offer several
distinct advantages over analog media. The quality of digital audio, images and video
signals are higher than that of their analog counterparts. Editing is easy because one can
access the exact discrete locations that need to be changed. Copying is simple with no loss
of fidelity. A copy of a digital media is identical to the original. With digital multimedia
distribution over World Wide Web, authentications are more threatened than ever due to the
possibility of unlimited copying. The easy transmission and manipulation of digital data
constitutes a real threat for information creators, and copyright owners want to be
compensated every time their work is used. Furthermore, they want to be sure that their
work is not used in an improper way (e. g. modified without their permission). For digital
data, copyright enforcement and content verification are very difficult tasks. One solution
would be to restrict access to the data using some encryption techniques. However,
2
encryption does not provide overall protection. Once the encrypted data are decrypted, they
can be freely distributed or manipulated.
Unauthorized use of data creates several problems. For example, if we visit http:\\www.
wallpaper.com, we observe that all the wallpaper images are created by the owners, which
are their Intellectual Property Right (IPR). Any user can download the wallpapers. Now,
consider that a user downloads the images and posts those images (either after modifying or
original) on his/her website. Three issues may arise in this situation:
1) How will the owner of wallpaper.com know that there is one more web server on
WWW posting their wallpapers?
2) If the owner knows about this fact, where shall he go to make a complaint?
3) The last but very important issue is that even if first two problems are resolved, how
the owner will prove the ownership on the wallpaper images posted on another
server?
The first issue is related to network technologies and involves issues like ‘web crawler’ and
‘pattern matching’ etc. Second issue is related to the international copyright laws and is
another very tricky issue. This thesis does not deal with these 2 issues. This thesis covers
the third issue, the authentication i.e. how to prove the ownership?
The above problem can be solved by hiding some ownership data into the multimedia data,
which can be extracted later to prove the ownership. This idea is implemented in bank
currency notes embedded with the watermark which is used to check the originality of the
note. The same “watermarking” concept may be used in multimedia digital contents for
checking the authenticity of the original content.
To begin with a quick background of watermarking, first we present the history of data
hiding and related terminologies. Then, we will move on to a discussion on the
3
watermarking, requirements that watermarking system must meet, types of the watermarking,
applications and then various attacks on a watermarking system.
1.1 DATA HIDING BACKGROUND The solution of the problem discussed above seems to lie in a technique that dates back to
ancient Egypt and Greece: data hiding or steganography. Steganography deals with the
methods of embedding data within a medium (host or cover medium) in an imperceptible
way. All forms of digital data (still images, audio, video, text documents and multimedia
documents) can be used as a cover medium for information hiding.
The history of steganography goes all the way back to the 5th Century. The earliest known
writings about steganography were by the Greek historian Herodotus. The historian relates
how a slave had a message tattooed on his head by Histiaeus who was trying to get a
message to his son-in-law Aristagoras. Once the slaves’ hair was long enough to cover the
message he was sent to Aristagoras in the city of Miletus [92].
Stegnography has been used in many different ways. The simplest was the use of invisible
inks that a person could use to send a message to another person without anyone else
knowing. Different forms of invisible ink were used to conceal messages. Some of the more
common forms of invisible ink have been lemon juice, milk, and urine to name a few. If
someone wanted to conceal a message, he would simply write a message, using one of these
inks, on a sheet of paper that already had something written on it. The person receiving the
message would then hold the paper over a flame and the transparent message would appear.
Image stegnography was done during the early twentieth century. During the Boer War in
South Africa, the British were using Lord Robert Baden-Powell as a scout. He was scouting
the Boer artillery bases mapping their positions. He took his maps and converted them into
pictures of butterflies with certain markings on the wings that were actually the enemies’
positions [92].
4
During World War II, Nazis introduced a new concept in espionage, which was called the
microdot. This simple device could conceal a full typewritten page within the size of a
common period. A microdot could hold valuable information such as charts, diagrams and
drawings.
Figure 1.1: Watermark on the bank currency note
Thus, stegnography is an area which is, more or less, a Hide-&-Seek game. Some important
data or information is hidden in another medium. The cover medium has no relationship
with the data or information hidden. Data or information which is hidden is not encrypted
also. The key issue in a stegnography system becomes that no one should suspect that a
particular medium is carrying any hidden data or information.
We can extend the stegnography concept for the authentication of digital multimedia data.
Digital multimedia data which has to be protected is now the cover medium and then we can
hide the copyright data into it. In this case, there will be two major requirements as follows:
1) Imperceptibility: After hiding the copyright data, cover medium should not be
affected, and
2) Robustness: No body should be able to remove the data without affecting the cover
medium.
Watermark symbol is added here to prove the originality
5
The copyright data may be termed as digital watermark data. This area of application of
stegnography is known as Digital Watermarking. Therefore, digital watermark is a
message/data/information which is embedded into digital content (audio, video, images or
text) that can be detected or extracted later. Such message/data/information mostly carries
the copyright or ownership information of the content. The process of embedding digital
watermark information into digital content is known digital watermarking.
Before moving further in this discussion, we must first understand the difference of the
digital watermarking with other related terms like stegnography, cryptography and digital
signature.
1.1.1 STEGANOGRAPHY VS WATERMARKING
Watermarking is the subset of Stegnography. In Stegnography, data which is hidden has no
relationship with the cover medium and the requirement from such a system is that no
suspicion should arise that a medium is carrying any hidden data. In watermarking, unlike
stegnography, the data which is hidden has relationship with the cover medium data. Data
hidden is the ownership data of the cover medium and there is no issue like suspecting that a
particular medium is carrying some copyright data.
As the purpose of stegnography is to have a covert communication between two parties i.e.
existence of the communication is unknown to a possible attacker, and a successful attack
shall detect the existence of this communication. On the contrary, watermarking, as opposed
to stegnography, requires a system to be robust against possible attacks. Other requirements
of watermarking are entirely different from stegnography and these are discussed in detail in
Section 1.3.
1.1.2 CRYPTOGRAPHY VS. WATERMARKING
Cryptography can be defined as the processing of information into an unintelligible form
known as encryption, for the purpose of secure transmission. Through the use of a “key”,
the receiver can decode the encrypted message (the process known as decryption) to retrieve
6
the original message. So, cryptography is about protecting the contents of the message. But
as soon as the data is decrypted, all the in-built security and data is ready to use.
Cryptography "scrambles" a message so that it can not be understood by unauthorized user.
This does not happen in watermarking. Neither the cover medium nor the copyright data
changes its meaning. Rather, copyright data is hidden to give the ownership information of
the medium in which it is hidden.
1.1.3 DIGITAL SIGNATURE VS. WATERMARKING
Digital signatures, like written signatures, are used to provide authentication of the associated
input, usually called a "message”. Digital signature is an electronic signature that can be used
to authenticate the identity of the sender of a message or the signer of a document, and
possibly to ensure that the original content of the message or document that has been sent is
unchanged. Digital signatures are easily transportable, cannot be imitated by someone else,
and can be automatically time-stamped. The ability to ensure that the original signed
message arrived means that the sender cannot easily repudiate it later. A digital signature can
be used with any kind of message, whether it is encrypted or not, simply so that the receiver
can be sure of the sender's identity and that the message arrived intact. A digital signature is
apart from the protected message, whereas a digital watermark is inside a multimedia
message. Both, digital signature and watermarking protect integrity and authenticity of a
document. Digital signature system is vulnerable to distortion but a watermark system has to
tolerate a limited distortion level.
So, to conclude, Watermarking is adding“ownership” information in multimedia contents to
prove the authenticity. This technology embeds a data, an unperceivable digital code,
namely the watermark, carrying information about the copyright status of the work to be
protected. Continuous efforts are being made to device efficient watermarking schema but
techniques proposed so far do not seem to be robust to all possible attacks and multimedia
data processing operations. The sudden increase in watermarking interest is most likely due
to the increase in concern over IPR. Today, digital data security covers such topics as access
control, authentication, and copyright protection for still images, audio, video, and
7
multimedia products. A pirate tries either to remove a watermark to violate a copyright or to
cast the same watermark, after altering the data, to forge the proof of authenticity. Generally,
the watermarking of still images, video, and audio demonstrate certain common fundamental
concepts.
1. 2 APPLICATION AREAS OF DIGITAL WATERMARKING Watermarking techniques may be relevant in the following application areas [26]:
1.2.1 COPYRIGHT PROTECTION
The primary use of watermarking is where an organization wishes to assert its ownership of
copyright for digital objects. This application is of great interest to ‘big media’
organizations, and of some interest to other vendors of digital information, such as news and
photo agencies. These applications require a minimal amount of information to be
embedded, coupled with a high degree of resistance to signal modification (since they may
be subjected to deliberate attack). For example, now a days, a news channel “AAJ-TAK” is
showing the animal’s clips (which are already shown on “Discovery” Channel) by hiding the
Discovery channel’s logo on the video clips. As per the law, The AAJ-TAK should show the
curtsey-sign and should pay the copyright fee to the Discovery channel. In such cases,
There is a strong need of watermarking as once the digital data is broadcasted, any body else
can start selling it without paying the IPR value to its owner.
1.2.2 COPY PROTECTION
Watermarking can be used as a strong tool to prevent illegal copying. For example, if an
audio CD has a watermark embedded into it, then any of the system (Hardware like DVD, or
software) can not make a copy of it, and even if it copies, the watermark data will not get
copied to new duplicate audio CD. Now the duplicate CD can be easily found because it
does not have watermark data. Some schemes have attempted to satisfy more complex copy
protection requirements. An early example is the Serial Copy Management System (SCMS),
introduced in the 1980s, which enabled a user to make a single digital audio tape of a
recording they had purchased but prevented the recording of further copies (i.e. second
8
generation) from that first copy. The scheme failed ultimately because not all manufacturers
of consumer equipment were prepared to implement the scheme in their products.
1.2.3 TEMPER DETECTION
In this application area, it is necessary to assure that the origin of a data object is
demonstrated and its integrity is proved. One example of temper detection is photographic
forensic information which may be presented as evidence in the court. Given the ease with
which digital images can be manipulated, there is a need to provide proof that an image has
not been altered. Such a mechanism could be built into a digital camera [29]. For example,
if a cop’s camera catches an over speeding vehicle then when proving the driver guilty in
front of the judge, the accused may claim that the video presented in the court is tempered
and the car shown in the video does not belong to him. A watermarking system which is
embedded in digital cameras may help to resolve the issue. If somebody tries to temper the
data, the watermark will get destroyed indicating that the data is tempered. In our country, a
well-known example is the “Tahalka-Scam”.
1.2.4 BROADCAST MONITORING
There are several types of organizations and individuals interested in monitoring the
broadcast of their interest. For example, advertisers want to ensure that they receive the exact
airtime that they have purchased from broadcasting firms. Musicians and actors want to
ensure that they receive accurate royalty payments for broadcasts of their performances and
copyright owners want to ensure that their property is not illegally rebroadcast by pirate
stations. In 1997, a scandal broke out in Japan regarding television advertising. At least two
stations had been routinely overbooking air time. Advertisers were paying for thousands of
commercials that were never aired [16]. The practice had remained largely undetected for
over twenty years because there were no systems in place to monitor the actual broadcast of
advertisements.
9
This broadcast monitoring can be implemented by putting a unique watermark in each video
or sound clip prior to broadcast. Automated monitoring stations can then receive broadcasts
and look for these watermarks identifying when and where each clip appears.
1.2.5 FINGERPRINTING
If monitoring and owner identification applications place the same watermark in all copies of
the same content, it may create a problem. If out of n number of legal buyers of a content,
one starts selling the contents illegally, it may be very difficult to catch who is redistributing
the contents without permission. Allowing each copy distributed to be customized for each
legal recipient can solve this problem. This capability allows a unique watermark to be
embedded in each individual copy. Now, if the owner finds an illegal copy, he can find out
who is selling his contents by finding the watermark which belongs to only singly legal
buyer. This particular application area is known as fingerprinting. This is potentially
valuable both as a deterrent to illegal use and as a technological aid to investigation.
1.2.6 ANNOTATION APPLICATIONS
In this applications area, watermarks convey object-specific information (“feature tags” or
“captions”) to users of the object. For example, patient identification data can be embedded
into medical images. These applications require relatively large quantities of embedded data.
While there is no need to protect against deliberate tampering. Normal use of the data object
may involve such transformations as image cropping or scaling and will require the use of a
technique that is resistant to those types of modification.
For more details of various watermarking applications, one may refer [20].
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1. 3 CHARACTERISTICS OF WATERMARKING SCHEMES An effective watermarking scheme should have the following characteristics:
1) Imperceptibility: In terms of watermarking, imperceptibility means that after inserting
the watermark data, cover medium should not alter much. In other words, the
presence of the watermark data should not affect the cover medium being protected.
If a watermarking scheme does not ensure this requirement, it may happen that after
inserting a watermark data in a cover medium (say an image), image quality may alter
which the owner of the image will never like that a protecting mechanism modifies
his work.
2) Robustness: Robustness of the watermark data means that the watermark data should
not be destroyed if someone performs the common manipulations as well as
malicious attacks. It is more of a property and also a requirement of watermarking
and its applicability depends on the application area.
3) Fragility: Fragility means that the watermark data is altered or disturbed up to a
certain extent when someone performs the common manipulations & malicious
attacks. Some application areas like temper detection may require a fragile
watermark to know that some tempering is done with his work. Some application
may require semi-fragility too. The semi-fragile watermark comprises a fragile
watermark component and a robust watermark component i.e. semi-fragile
watermarks are robust to some attacks but fragile to others attacks.
4) Resilient to common signal processing: The watermark should be retrievable even if
common signal processing operations are applied to the watermarked cover medium
data. These operations include digital-to-analog and analog-to-digital conversion (i.e.
taking the printout of an image and then scan it to create another digital copy of the
image), re-sampling, re-quantization (including dithering and recompression), and
common signal enhancements such as image contrast, brightness and color
adjustment, or audio bass and treble adjustment, high pass and low pass filtering,
histogram equalization of an image and format conversion (BMP image to JPEG
image, MPEG movie to WMV movie, mp3 song to mp4 etc.)
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5) Resilient to common geometric distortions (image and video data): Watermarks in
image and video data should also be immune from geometric image operations such
as rotation, translation, cropping and scaling. This property is not required for audio
watermarking.
6) Robust to subterfuge attacks (collusion and forgery): In addition, the watermark
should be robust to collusion attack. Multiple individuals, who possess a watermarked
copy of the data, may collude their watermark copies to destroy the watermark
presence and can generate a duplicate of the original copy. Further, if a digital
watermark is to be used in litigation, it must be impossible for colluders to combine
their images to generate a different valid watermark.
7) Unambiguousness: Retrieval of the watermark should unambiguously identify the
owner. Furthermore, the accuracy of owner identification should not degrade much
in the case of an attack. The Unzign and Stirmark [97] have shown remarkable
success in removing data embedded by commercially available programs.
Watermarking of watermarked image (re-watermarking) is also a major threat [97].
1.4 TYPES OF DIGITAL WATERMARKS Prof. S. Mohanty presents a very good classification of watermarking areas in his paper [62].
We can classify the types of watermarking based on the cover medium, embedding domain,
perception and application domain. Figure 1.2 shows the various classifications of
watermarking.
Based on their embedding domain, watermarking schemes can be classified as follows:
1) Spatial Domain: The watermarking system directly alters the main data elements (like
pixels in an image) to hide the watermark data.
2) Transformed Domain: The watermarking system alters the frequency transforms of
data elements to hide the watermark data. This has proved to be more robust than the
spatial domain watermarking.
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3) Feature Domain: The watermarking system takes into account the region, boundary
and object characteristics. It presents better detection and recovery from attacks.
Figure 1.2: Various classifications of watermarking
Watermarking techniques can also be divided into four categories, according to the type of
document to be watermarked, as follows.
1) Image Watermarking: Figure 1.3 and 1.4 represent the general scheme of an image
watermarking, embedding and decoding (specifically key based, invisible and fragile)
13
system respectively. ‘E’ represents the watermarking embedding algorithm and ‘D’
represents the watermarking decoding algorithm.
2) Other types of watermarking, according to the type of document to be watermarked
are Video Watermarking, Audio Watermarking and Text Watermarking.
Figure 1.3: Image watermark embedding scheme
Figure 1.4: Image watermark detection scheme
According to the human perception, the digital watermarks can be divided into 4 different
types: Visible watermark, Invisible-Robust watermark, Invisible-Fragile watermark, and dual
watermark. Visible watermark is a secondary translucent overlaid into the primary image.
The watermark appears visible to a casual viewer on a careful inspection. The invisible-
robust watermark is embedded in such a way that alternations made to the pixel value are
perceptually not noticed and it can be recovered only with appropriate decoding mechanism.
The fragile watermark is embedded in such a way that any manipulation or modification of
the image would alter or destroy the watermark. Dual watermark is a combination of a
visible and an invisible watermark [8].
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According to application domain, Source-based watermarks are desirable for ownership
identification or authentication where a unique watermark identifies the owner. A source-
based watermark could be used for authentication and to determine whether a received image
or other electronic data has been tampered. The watermark could also be destination based
where each distributed copy gets a unique watermark identifying the particular buyer. The
destination based watermark could be used to trace the buyer in the case of illegal reselling.
This is used in fingerprinting. A watermark is said private if only authorized readers can
detect it. In other words, in private watermarking, a mechanism is envisaged that makes it
impossible for unauthorized people to extract the watermark. A watermarking algorithm is
said blind if it does not resort to the comparison between the original non-marked and the
marked document to recover the watermark. Conversely, a watermarking algorithm is said
non-blind if it needs the original data to extract the information contained in the watermark.
The definition of invertible and quasi-invertible is more technical and can be given as
follows [2]:
If E is the Embedding algorithm, D is detection algorithm, Cδ is Comparator function, I is
original cover image, Î is watermarked image, J is recovered attacked image, S is watermark
signal and S’ is extracted watermark data, then:
1) E (I, S) = Î
2) D (J, I) = S’ or D (J) = S’
3) Comparator Cδ:
A watermarking scheme (E, D, Cδ) is invertible if:
1) Inverse mapping E-1 does exist such that E-1 (Î) = (Î’, S’) &E (Î’, S’) = Î;
2) E-1 is computational feasible;
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3) S’ is an allowed watermark;
4) Î and Î’ are perceptually similar; and
5) Comparator output Cδ (D (Î, Î’), S’) = 1
Otherwise the watermarking scheme is non-invertible.
A watermarking scheme (E, D, Cδ) is quasi-invertible if:
1) Properties for invertible watermarking schemes apply;
2) Only difference E (Î’, S’) = Î’’ ≠ Î; and
3) Î’’ and Î perceptually similar.
Otherwise the watermarking scheme is non-quasi-invertible. A Non-invertible scheme can
be quasi-invertible and Non-quasi-invertibility implies non-invertibility.
1.5 STRUCTURE OF THE THESIS This thesis comprises of the following chapters:
Chapter 2 describes the image watermarking literature survey and problem statement.
Chapter 3 describes the preliminaries (like background of JPEG compression, 2D–DCT and
DWT, image quality parameter, some standard watermarking techniques which are used to
compare the performances of the proposed techniques etc and test images data). The
watermarking techniques for gray images have been proposed in Chapter 4. Chapter 5
describes the proposed watermarking techniques and issues related to colored BMP images.
In Chapter 6, the proposed watermarking techniques for JPEG images have been given.
Finally the summary of results, conclusions and future work is given in Chapter 7 followed
by references, author’s publications and synopsis at the end.
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17
CHAPTER-2
IMAGE WATERMARKING LITERATURE SURVEY
Within the field of watermarking, image watermarking particularly has attracted lot of
attention in the research community. Most of the research work is dedicated to image
watermarking as compared to audio and video. There may be 3 reasons for it. Firstly,
because of ready availability of the test images, secondly because it carries enough
redundant information to provide an opportunity to embed watermarks easily, and lastly,
it may be assumed that any successful image watermarking algorithm may be upgraded
for the video also.
Images are represented/stored in spatial domain as well as in transform domain. The
transform domain image is represented in terms of its frequencies; whereas, in spatial
domain it is represented by pixels. In simple terms, transform domain means the image is
segmented into multiple frequency bands. To transfer an image to its frequency
representation, we can use several reversible transforms like Discrete Cosine Transform
(DCT), Discrete Wavelet Transform (DWT), or Discrete Fourier Transform (DFT). Each
of these transforms has its own characteristics and represents the image in different ways.
Watermarks can be embedded within images by modifying these values, i.e. the
transform domain coefficients. In case of spatial domain, simple watermarks could be
embedded in the images by modifying the pixel values or the Least Significant Bit (LSB)
values. However, more robust watermarks could be embedded in the transform domain of
images by modifying the transform domain coefficients. In 1997 Cox et al. presented a
paper “Secure Spread Spectrum Watermarking for Multimedia” [19], one of the most
cited paper (cited 2985 times till April’ 2008 as per Google Scholar search), and after that
most of the research work is based on this work. Even though spatial domain based
techniques can not sustain most of the common attacks like compression, high pass or
low pass filtering etc., researchers present spatial domain based schemes. Firstly, brief
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introductions of some classical well-known spatial domain based schemes are being
given as follows [19]:
2.1 SPATIAL DOMAIN BASED WATERMARKING SCHEMES
2.1.1 LSB BASED SCHEMES
In their paper, Macq and Quisquater [60] briefly discussed the issue of watermarking
digital images as part of a general survey on cryptography and digital television. The
authors provided a description of a procedure to insert a watermark into the least
significant bits of pixels located in the vicinity of image contours. Since it relies on
modifications of the least significant bits, the watermark is easily destroyed. Further, their
method is restricted to images, in that it seeks to insert the watermark into image regions
that lie on the edge of contours.
Rhoads [79] described a method that adds or subtracts small random quantities from each
pixel. Addition or subtraction is determined by comparing a binary mask of bits with the
LSB of each pixel. If the LSB is equal to the corresponding mask bit, then the random
quantity is added, otherwise it is subtracted. The watermark is subtracted by first
computing the difference between the original and watermarked images and then by
examining the sign of the difference, pixel by pixel, to determine if it corresponds to the
original sequence of additions and subtractions. This method does not make use of
perceptual relevance, but it is proposed that the high frequency noise be prefiltered to
provide some robustness to lowpass filtering. This scheme does not consider the problem
of collusion attacks.
2.1.2 PATCH WORK BASED SCHEMES
Another, well known spatial domain based scheme is patchwork-based technique given
by Bender et al. [7]. They described two watermarking schemes. The first is a statistical
method called patchwork. Patchwork randomly chooses pairs of image points, and
increases the brightness at one point by one unit while correspondingly decreasing the
brightness of another point. The second method is called “texture block coding” wherein
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a region of random texture pattern found in the image is copied to an area of the image
with similar texture. Autocorrelation is then used to recover each texture region. The
most significant problem with this scheme is that it is only appropriate for images that
possess large areas of random texture. The scheme could not be used on images of text.
Other Patchwork based algorithm can be found in [110, 124].
2.1.3 CORRELATION BASED WATERMARKING SCHEMES
The most straightforward way to add a watermark to an image in the spatial domain is to
add a pseudorandom noise pattern to the luminance values of its pixels. Many methods
are based on this principle [6, 11, 27, 33-34, 53, 68, 70, 91, 95, 114-117].
2.1.3.1 CORRELATION BASED SCHEMES WITH 1 PN SEQUENCE: A well
known technique for watermark embedding is to exploit the correlation properties of
additive pseudo-random noise patterns as applied to an image [42, 52]. A Pseudo-random
Noise (PN) pattern W (x, y) is added to the cover image I (x, y), according to the
Equation 2.1 given below:
),(*),(),( yxWkyxIyxI w += ……………………………………………………… (2.1)
In Equation 2.1, k denotes a gain factor and IW the resulting watermarked image.
Increasing k increases the robustness of the watermark at the expense of the quality of the
watermarked image. To retrieve the watermark, the same pseudo-random noise generator
algorithm is seeded with the same key, and the correlation between the noise pattern and
possibly watermarked image is computed. If the correlation exceeds a certain threshold T,
the watermark is detected, and a single bit is set. This method can easily be extended to a
multiple-bit watermark by dividing the image into blocks and performing the above
procedure independently on each block.
2.1.3.2 CORRELATION-BASED IMAGE WATERMARKING SCHEMES WITH
2PN SEQUENCES: This basic algorithm, as given in previous section, can be improved
in a number of ways. First, the notion of a threshold being used for determining a logical
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“1” or “0” can be eliminated by using two separate pseudo-random noise patterns. One
pattern is designated a logical “1” and the other a logical “0”. The above procedure is
then performed once for each pattern, and the pattern with the higher resulting correlation
is used. This increases the probability of correct detection, even after the image has been
subject to attack [42, 52].
2.1.3.3 IMAGE WATERMARKING USING PRE-FILTERING: We can further
improve the basic algorithm by pre-filtering the image before applying the watermark. If
we can reduce the correlation between the cover image and the PN sequence, we can
increase the immunity of the watermark to additional noise. By applying the edge
enhancement filter shown below in Figure 2.1, the robustness of the watermark can be
improved with no loss of capacity and very little reduction of image quality [42, 52].
−−−−−−−−
=1111101111
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edgeF
Figure 2.1: FIR Edge Enhancement Pre-Filter
2.1.4 CDMA BASED IMAGE WATERMARKING SCHEME
Rather than determining the values of the watermark from “blocks” in the spatial domain,
we can employ CDMA spread-spectrum schemes to scatter each of the bits randomly
throughout the cover image, thus increasing capacity and improving resistance to
cropping. The watermark is first formatted as a long string rather than a 2D image. For
each value of the watermark, a PN sequence is generated using an independent seed.
These seeds could either be stored or themselves generated through PN methods. The
summation of all of these PN sequences represents the watermark, which is then scaled
and added to the cover image [42, 52].
To detect the watermark, each seed is used to generate its PN sequence which is then
correlated with the entire image. If the correlation is high, that bit in the watermark is set
to “1”, otherwise a “0”. The process is then repeated for all the values of the watermark.
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CDMA improves on the robustness of the watermark significantly but it requires more
computation.
2.1.5 OTHER SPATIAL DOMAIN BASED WATERMARKING SCHEMES
In [104], a method that embeds a binary watermark image in the spatial domain is
proposed. A spatial transform that maps each pixel of the watermark image to a pixel of
the host image, is used. Chaotic spread of watermark image pixels in the host image is
achieved by “toral automorphisms”. For watermark embedding, the intensity of the
selected pixels is modified by an appropriate function that takes into account
neighborhood information in order to achieve watermark robustness to modifications. For
detection, a suitable function is applied on each of the watermarked pixels to determine
the binary digit (0 or 1) that has been embedded. The inverse spatial transform is then
used to reconstruct the binary watermark image.
In the method proposed in [69], the image is split into two random subsets A and B and
the intensity of pixels in A is increased by a constant embedding factor k. Watermark
detection is performed by evaluating the difference of the mean values of the pixels in
subsets A and B. This difference is expected to be equal to k for a watermarked image
and equal to zero for an image that is not watermarked. Hypothesis testing can be used to
decide for the existence of the watermark. The above algorithm is vulnerable to lowpass
operations. Extensions to above algorithm are proposed in [64]. According to this
paper, the robustness of the method can be increased by grouping pixels so as to form
blocks of certain dimensions to enhance the low pass characteristics of the watermark
signal. Alternatively, one can take advantage of the fact that different embedding factor
can be used for each pixel, to shape appropriately the watermark signal. An optimization
procedure that calculates the appropriate embedding value for each pixel so that the
energy of the watermark signal is concentrated at low frequencies is proposed.
Constraints that ensure that the watermark signal is invisible can be incorporated in the
optimization procedure.
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In [45] the authors derived analytical expressions for the probabilities P-, P+ of false
negative and false positive watermark detection. Their model assumes an additive
watermark and a correlator-based detection stage. Both, the white watermarks and
watermarks having low pass characteristics, are considered. The host image is treated as
noise, assuming a first order separable autocorrelation function. The probabilities P-, P+
are expressed in terms of the watermark to image power ratio. The authors conclude that
detection error rates are higher for watermarks with low pass characteristics.
In last 12 years, number of publications in this area is increasing very rapidly and no
survey can cover all the presented schemes, but there are some very good survey papers
and interested reader may explore the papers [3, 13, 54, 76]. We are limiting the
discussion of the spatial domain based schemes here.
2.2 TRANSFORMED DOMAIN BASED SCHEMES As presented in literature, transformed domain based watermarking schemes are more
robust as compared to simple spatial domain watermarking schemes. Such algorithms are
robust against simple image processing operations like low pass filtering, brightness and
contrast adjustment, blurring etc. However, they are difficult to implement and are
computationally more expensive. We can use either of Discrete Fourier Transform
(DFT), Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) but
DCT is the most exploited one. A General transformed domain based scheme, as
presented by Cox, is shown in Figure 2.2. A very good discussion on DCT/DWT/DFT
based watermarking schemes is given in [76].
2.2.1 DFT BASED WATERMARKING SCHEMES
We start from DFT. There are few algorithms that modify these DFT magnitude and
phase coefficients to embed watermarks. Ruanaidh et al. proposed a DFT watermarking
scheme in which watermark is embedded by modifying the phase information within the
DFT. It has been shown that phase based watermarking is robust against image contrast
operation [114]. Later Ruanaidh and Pun showed how Fourier Mellin transform could be
23
used for digital watermarking. Fourier Mellin transform is similar to applying Fourier
Transform to log-polar coordinate system for an image.
This scheme is robust against geometrical attacks [116]. De Rosa et al. proposed a
scheme to insert watermark by directly modifying the mid frequency bands of the DFT
magnitude component [115]. Ram kumar et al. also presented a data hiding scheme based
on DFT, where they modified the magnitude component of the DFT coefficients. Their
simulations suggest that magnitude DFT survives practical compression which can be
attributed to the fact that most practical compression schemes try to maximize the PSNR.
Hence using magnitude DFT is a way to exploit the hole in most practical compression
schemes.
Figure 2.2: A General Frequency domain based watermarking model as presented by Cox [19]
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The proposed scheme is shown to be resistant to Joint Photographic Expert Group
(JPEG) and (Set Partitioning In Hierarchical Trees) SPIHT compression [68]. Lin et al.
presented a RST resilient watermarking algorithm. In their algorithm, the watermark is
embedded in the magnitude coefficients of the Fourier transform re-sampled by log-polar
mapping. The scheme is, however, not robust against cropping and shows weak
robustness against JPEG compression (Q = 70) [53]. Solachidis and Pitas presented a
novel watermarking scheme. They embed a circularly symmetric watermark in the
magnitude of the DFT domain [8]. Since the watermark is circular in shape with its centre
at image center, it is robust against geometric rotation attacks. The watermark is centered
around the mid frequency region of the DFT magnitude. Neighborhood pixel variance
masking is employed to reduce any visible artifacts. The scheme is computationally not
expensive to recover from rotation. Robustness against cropping, scaling, JPEG
compression, filtering, noise addition and histogram equalization is demonstrated. A
semi-blind watermarking scheme has been proposed by Ganic and Eskicioglu [30]. They
embed circular watermarks with one in the lower frequency while the other is in the
higher frequency.
2.2.2 DCT BASED WATERMARKING SCHEMES
DCT domain watermarking can be classified into Global DCT watermarking and Block
based DCT watermarking. One of the first algorithms presented by Cox et al. [19] used
global DCT approach to embed a robust watermark in the perceptually significant portion
of the Human Visual System (HVS). Embedding in the perceptually significant portion of
the image has its own advantages because most compression schemes remove the
perceptually insignificant portion of the image. In spatial domain it represents the LSB.
However in the frequency domain it represents the high frequency components.
As described in [76], steps in DCT Block Based Watermarking Algorithm are:
1) Segment the image into non-overlapping blocks of 8x8;
2) Apply forward DCT to each of these blocks;
3) Apply some block selection criteria (e.g. HVS);
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4) Apply coefficient selection criteria (e.g. highest);
5) Embed watermark by modifying the selected coefficients; and
6) Apply inverse DCT transform on each block.
Most DCT based algorithms differ with each other on account of step 3 and 4 i.e. they
differ either in the block selection criteria or coefficient selection criteria. Initially, Koch,
Rindfrey, and Zhao [7] proposed a method for watermarking images. In that method, they
break up an image into 8x8 blocks and compute discrete cosine transform (DCT) of each
of these blocks. A pseudorandom subset of the blocks is chosen and then in each such
block, a triplet of frequencies is selected from one of 18 predetermined triplets and
modified so that their relative strengths encode a ‘1’ or ‘0’ value. The 18 possible triplets
are composed by selection of three out of eight predetermined frequencies within the 8x8
DCT block. The choice of the eight frequencies to be altered within the DCT block is
based on a belief that the “middle frequencies have moderate variance,” i.e. they have
similar magnitude. This property is used to allow the relative strength of the frequency
triplets to be altered without requiring a modification that would be perceptually
noticeable.
Several DCT based schemes are presented in [8, 17-19, 21, 37, 71, 74, 81, 99, 118].
Using the DCT, an image can easily be split up in pseudo frequency bands so that the
watermark can conveniently be embedded in the most important middle band frequencies.
Furthermore, the sensitivity of the HVS to the DCT based images has been extensively
studied, which resulted in the recommended JPEG quantization Table [112]. These
results can be used for predicting and minimizing the visual impact of the distortion
caused by the watermark. Finally, the block-based DCT is widely used for image and
video compression. By embedding a watermark in the same domain as the compression
scheme used to process the image (in this case in the DCT domain), we can anticipate
lossy compression because we are able to anticipate which DCT coefficients will
be discarded by the compression scheme. Furthermore, we can exploit the DCT
decomposition to make real-time watermark applications.
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Further improvements for DCT-domain correlation-based watermarking systems'
performance could be achieved by using watermark detectors based on generalized
Gaussian model instead of the widely used pure Gaussian assumption [35]. By
performing a theoretical analysis for DCT-domain watermarking methods for images, the
authors in [35] provided analytical expressions which could be used to measure
beforehand the performance expected for a certain image and to analyze the influence of
the image characteristics and system parameters (e.g. watermark length) on the final
performance. Furthermore, the result of this analysis may help in determining the proper
detection threshold T to obtain a certain false positive rate. The authors in [35] claimed
that by abandoning the pure Gaussian noise assumption, some substantial performance
improvements could be obtained.
In [4], the authors embedded a watermark signal domain by modifying a number of
predefined DCT coefficients. They used a weighting factor to weight the watermark
signal in the spatial domain according to HVS characteristics. In [75] authors embedded
watermark data in DCT Difference (JND) as predicted domain in perceptually meaningful
way and used the Just Noticeable by model reported in [108].
2.2.2.1 THE MIDDLE-BAND COEFFICIENT EXCHANGE SCHEME [42, 52]:
The middle-band frequencies (FM) of an 8x8 DCT block are shown in Figure 2.3. In this
Figure, FL is used to denote the lower frequency components of the block and FH is used
to denote the higher frequency components. FM is chosen as embedding region to
provide additional resistance to lossy compression techniques, while avoiding significant
modification of the cover image. First, 8x8 DCT of an original image is taken. Then, two
locations DCT (u1, v1) and DCT (u2, v2) are chosen from the FM region for comparison of
each 8 x 8 block. These locations are selected based on the recommended JPEG
quantization table shown in Figure 2.4. If two locations are chosen such that they have
identical quantization values, then any scaling of one coefficient will scale the other by
the same factor to preserve their relative strength. It may be observed from Figure 2.4,
that coefficients at location (4, 1) and (3, 2) or (1, 2) and (3, 0) are more suitable
candidates for comparison because their quantization values are equal. The DCT block
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will encode a “1” if DCT (u1, v1) > DCT (u2, v2); otherwise it will encode a “0”. The
coefficients are swapped if the relative size of coefficients does not agree with the bit that
is to be encoded [42, 52].
Thus, instead of embedding any data, this scheme is hiding watermark data by means of
interpreting “0” or “1” with relative values of two fixed locations in middle frequency
region.
FL
FM
FH
Figure 2.3: Frequency regions in 8 x 8 DCT
Swapping of such coefficients will not alter the watermarked image significantly, as it is
generally believed that DCT coefficients of middle frequencies have similar magnitudes.
Further, the robustness of the watermark can be improved by introducing a watermark
“strength” constant k, such that DCT (u1, v1) – DCT (u2, v2) > k. If coefficients do not
meet these criteria, they are modified by the use of random noise to satisfy the relation.
Increasing k thus reduces the chance of detection errors at the expense of additional
image degradation. By increasing k, larger coefficients remain larger even after lot of
compression and thus help in decoding because their relative values decide the decoding
of the watermark data.
While extracting the watermark, again the 8x8 DCT of image in taken in which “1” is
decoded if DCT (u1, v1) > DCT (u2, v2); otherwise a “0” is decoded.
28
Figure 2.4: JPEG Quantization matrix
Limitation of middle-band coefficient exchange scheme: Experimental results show that
Middle-Band Coefficient Exchange is quite efficient against JPEG compression,
Cropping, Noising and other common image manipulation operations. But above scheme
has one serious drawback. If only one pair of coefficient is used (say (4, 1) and (3, 2)) to
hide the watermark data, then it is vulnerable to collusion attack. By analyzing four or
five watermarked copies of an image, one can easily find out that these coefficients
always have a certain pattern and attacker can predict the watermark as well as destroy it.
2.2.2.2 DCT-CDMA BASED IMAGE WATERMARKING [42, 52]: In this
technique authors embedded a PN sequence W into the middle frequencies of the DCT
block. A DCT block can be modulated using the Equation 2.2.
∉∈+
=M
MyxyxyxW FvuvuyIx
FvuvuWkvuIvuI
,),,(,,),,(*),(
),( ,,, ………………………………………….. (2.2)
For each 8 x 8 block of the image, the DCT for the block is first calculated. In that block,
the middle frequency components FM are added to the PN sequence W, multiplied by a
gain factor k. Each block is then inverse-transformed to give the final watermarked image
IW.
The watermarking procedure is made somewhat more adaptive by slightly altering the
embedding process to the method shown in Equation 2.3.
29
∉∈+
=M
MyxyxyxW FvuvuyIx
FvuvuWkvuIvuI
,),,(,,)),,(*1(*),(
),( ,,, ……………………………... (2.3)
This slight modification scales the strength of the watermarking based on the size of the
particular coefficients being used. Larger values of k can thus be used for coefficients of
higher magnitude; in effect strengthening the watermark in regions that can afford it;
weakening it in other regions.
For detection, the image is broken up into same 8x8 blocks and a DCT is taken. The same
PN sequence is then compared to the middle frequency values of the transformed block.
If the correlation between the sequences exceeds some threshold T, a “1” is detected for
that block; otherwise a “0” is detected. Again k denotes the strength of the watermarking,
where increasing k increases the robustness of the watermark at the expense of quality.
2.2.3 DWT BASED WATERMARKING SCHEMES
If watermarking techniques can exploit the characteristics of the Human Visual System
(HVS), it is possible to hide watermarks with more energy in an image, which makes
watermarks more robust. From this point of view, the DWT is a very attractive transform,
because it can be used as a computationally efficient version of the frequency models for
the HVS [5]. For instance, it appears that the human eye is less sensitive to noise in high
resolution DWT bands and in the DWT bands having an orientation of 45° (i.e., HH
bands). Furthermore, DWT image and video coding, such as embedded zero-tree
wavelet (EZW) coding, are included in the upcoming image and video compression
standards, such as JPEG2000 [112]. Thus DWT decomposition can be exploited to
make a real-time watermark application.
Many approaches apply the basic schemes described at the beginning of this section
to the high resolution DWT bands, LH, HH, and HL [35, 40]. A large number of
algorithms operating in the wavelet domain have been proposed till date.
30
Figure 2.5: 1-Scale and 2-Scale 2-Dimensional Discrete Wavelet Transform
2.2.3.1 CDMA-DWT BASED WATERMARKING SCHEME: This scheme is the
most straightforward scheme which is similar to embedding scheme to that used in the
DCT-CDMA scheme. The embedding of a CDMA sequence in the frequency bands is
shown in Equation 2.4.
∈∈+
=HHLLvuWLHHLvuxWW
Ii
iiivuW ,,
,,,,
α………………………………………………. (2.4)
where Wi denotes the coefficient of the transformed image, xi the bit of the watermark to
be embedded, and α a scaling factor. To detect the watermark, same pseudo-random
sequence used in CDMA generation is generated and its correlation is determined with
the two transformed frequency bands. If the correlation exceeds some threshold T, the
watermark is detected.
This can be easily extended to multiple bit messages by embedding multiple watermarks
into the image. In the spatial version, a separate seed is used for each PN sequence,
which are then added to the frequency coefficients. During detection, if the correlation
exceeds T for a particular sequence a “1” is recovered; otherwise a “0”. The recovery
process then iterates through the entire PN sequence until all the bits of the watermark
have been recovered.
31
DWT based watermarking schemes follow the same guidelines as DCT based schemes,
i.e. the underlying concept is the same; however, the process to transform the image into
its transform domain varies and hence the resulting coefficients are different. Wavelet
transforms use wavelet filters to transform the image. There are many available filters,
although the most commonly used filters for watermarking are Haar Wavelet Filter,
Daubechies Orthogonal Filters and Daubechies Bi-Orthogonal Filters. Each of these
filters decomposes the image into several frequencies. Single level decomposition gives
four frequency representations of the images. In their paper [76], authors presented a
survey of wavelet based watermarking algorithms. They classify algorithms based on
decoder requirements as Blind Detection or Non-blind Detection. As mentioned earlier
blind detection doesn't require the original image for detecting the watermarks; however,
non-blind detection requires the original image.
2.2.3.2 DWT BASED BLIND WATERMARK DETECTION: Lu et al. [58]
presented a novel watermarking technique called as "Cocktail Watermarking". This
technique embeds dual watermarks which compliment each other. This scheme is
resistant to several attacks, and no matter what type of attack is applied; one of the
watermarks can be detected. Furthermore, they enhance this technique for image
authentication and protection by using the wavelet based Just Noticeable Distortion
(JND) values. Hence this technique achieves copyright protection as well as content
authentication simultaneously. Zhu et al. [126] presented a multi-resolution watermarking
scheme for watermarking video and images. The watermark is embedded in all the high
pass bands in a nested manner at multiple resolutions. This scheme doesn't consider the
HVS aspect; however, Kaewkamnerd and Rao [43-44] improve this scheme by adding
the HVS factor in account. Voyatzis and Pitas [104], who presented the "toral
automorphism" concept, provide a technique to embed binary logo as a watermark which
can be detected using visual models as well as by statistical means. So, in case the image
is degraded too much and the logo is not visible, it can be detected statistically using
correlation. Watermark embedding is based on a chaotic (mixing) system. Original image
is not required for watermark detection. However, the watermark is embedded in spatial
domain by modifying the pixel or luminance values.
32
A similar approach is presented for the wavelet domain [121], where the authors
proposed a watermarking algorithm based on chaotic encryption. Zhao et al.[125]
presented a dual domain watermarking technique for image authentication and image
compression. They used the DCT domain for watermark generation and DWT domain for
watermark insertion. A soft authentication watermark is used for tamper detection and
authentication while a chrominance watermark is added to enhance compression. They
use the orthogonality of DCT-DWT domain for watermarking [125].
2.2.3.3 DWT BASED NON-BLIND WATERMARK DETECTION: This technique
requires the original image for detecting the watermark. Most of the schemes found in
literature use a smaller image as a watermark and hence cannot use correlation based
detectors for detecting the watermark; as a result they rely on the original image for
informed detection. The size of the watermark image (normally a logo) normally is
smaller compared to the host image. Xia et al. presented a wavelet based non-blind
watermarking technique for still images where watermarks are added to all bands except
the approximation band. A multi-resolution based approach with binary watermarks is
presented here [37]. Here, both the watermark logo as well as the host image is
decomposed into sub bands and later embedded. Watermark is subjectively detected by
visual inspection; however, an objective detection is employed by using normalized
correlation. Lu et al. presented another robust watermarking technique based on image
fusion. They embedded a grayscale and binary watermark which is modulated using the
"toral automorphism" described in [106]. Watermark is embedded additively. The
novelty of this technique lies in the use of secret image instead of host image for
watermark extraction and use of image dependent and image independent permutations to
de-correlate the watermark logos [57]. Raval and Rege presented a multiple
watermarking scheme. The authors argued that if the watermark is embedded in the low
frequency components, it is robust against low pass filtering, lossy compression and
geometric distortions. On the other hand, if the watermark is embedded in high frequency
components, it is robust against contrast and brightness adjustment, gamma correction,
histogram equalization and cropping and vice-versa. Thus, to achieve overall robustness
33
against a large number of attacks, the authors proposed to embed multiple watermarks in
low frequency and high frequency bands of DWT [78].
Kundur and Hatzinakos [50] presented image fusion watermarking scheme. They used
salient features of the image to embed the watermark. They used a saliency measure to
identify the watermark strength and later embedded the watermark additively.
Normalized correlation is used to evaluate the robustness of the extracted watermark.
Later the authors proposed another scheme termed as FuseMark [51] which includes
minimum variance fusion for watermark extraction. Here, they propose to use a
watermark image whose size is a factor of the host by 2xy. Tao and Eskicioglu presented
an optimal wavelet based watermarking scheme. They embedded binary logo watermark
in all the four bands. But they embedded the watermarks with variable scaling factor in
different bands. The scaling factor is high for the LL sub band but for the other three
bands it is lower. The quality of the extracted watermark is determined by Similarity
Ratio measurement for objective calculation [100]. Ganic and Eskicioglu inspired by
Raval and Rege [78] proposed a multiple watermarking scheme based on DWT and
Singular Value Decomposition (SVD). They argued that the watermark embedded by
Raval and Rege [78] scheme was visible in some parts of the image especially in the low
frequency areas, which reduced the commercial value of the image. Hence they
generalized their scheme by using all the four sub bands and embedding the watermark in
SVD domain. The core technique is to decompose an image into four sub bands and then
applying SVD to each band. The watermark is actually embedded by modifying the
singular values from SVD [30].
2.3 RECENT METHODOLOGIES
Now-a-days, researchers are focusing on mixing of spatial and transformed domains (i.e.
combinations of DFT, DWT and DCT) concepts and also applying more and more
mathematical and statistical model, and other interdisciplinary approaches in
watermarking: for example use of chaotic theory, fractal image coding etc. In this section
we are presenting the brief of few recent watermarking algorithms.
34
In [103], authors presented a reversible watermarking scheme for the 2D-vector data
(point coordinates), which are used in geographical information related applications. This
reversible watermarking scheme exploits the high correlation among points in the same
polygon in a map and achieves the reversibility of the whole scheme by an 8-point
integer DCT, which ensures that the original 2D-vector data can be watermarked during
the watermark embedding process and then perfectly restored during the watermark
extraction process. In this scheme, author used an efficient highest frequency coefficient
modification technique in the integer DCT domain to modulate the watermark bit “0” or
“1”, which can be determined during extraction without using any additional information.
To alleviate the visual distortion in the watermarked map caused by the coefficient
modification, they proposed an improved reversible watermarking scheme based on the
original coefficient modification technique. Combined with this improved scheme, the
embedding capacity could be greatly increased while the watermarking distortion is
reduced as compared to the original coefficient modification scheme presented in [103].
In [65], authors presented zero-knowledge watermark detectors. Current detectors are
based on a linear correlation between the asset features and a given secret sequence. This
detection function is susceptible of being attacked by sensitivity attacks for which zero-
knowledge does not provide protection. In this work, a new zero-knowledge watermark
detector robust to sensitivity attacks is presented, using the generalized Gaussian
Maximum Likelihood (ML) detector as the basis. The inherent robustness that this
detector presents against sensitivity attacks, together with the security provided by the
zero-knowledge protocol that conceals the keys that could be used to remove the
watermark or to produce forged assets, results in a robust and secure protocol.
Additionally, two new zero-knowledge proofs for modulus and square root calculation
are presented. They serve as building blocks for the zero-knowledge implementation of
the Generalized Gaussian ML detector, and also open new possibilities in the design of
high level protocols.
35
If digital watermarking is to adequately protect content in systems which provide
resolution and quality scalability, then the watermarking algorithms must provide both
resolution and quality scalability. Although there exists a trade off between resolution
and quality scalability, it has been demonstrated that it is possible to achieve both types
by taking advantage of human visual system characteristics to increase quality scalability
without compromising resolution scalability. Watermarking algorithms considering this
problem have been proposed; however, they tend to focus on a single type of scalability,
resolution [96, 120] or quality [12, 98]. Peng et al. [66] considered both types, but their
algorithm deals exclusively with authentication and is not a watermarking algorithm. In
their work [67] authors focused on providing a spread spectrum watermarking algorithm
which had both resolution and quality scalability demonstrated through experimental
testing using the JPEG2000 compression algorithm. To alleviate this trade off, they began
with a non-adaptive resolution scalable algorithm and exploited the contrast sensitivity
and texture masking characteristics of the HVS to construct an HVS adaptive algorithm
that has good quality scalability. Their algorithm is specifically designed to concentrate
on textured regions only, avoiding the visible distortions, which may occur when strength
increases are applied to edges. Furthermore, this texture algorithm is applied in the
wavelet domain but uses only a single resolution for each coefficient to be watermarked.
In their work [126], authors presented a new image adaptive watermarking scheme based
on perceptually shaping watermark block wise. Instead of the global gain factor, a
localized one is used for each block. Watson’s DCT-based visual [109] model is adopted
to measure the distortion of each block introduced by watermark, rather than the whole
image. With the given distortion constraint, the maximum output value of linear
correlation detector is derived in one block, which demonstrated the reachable maximum
robustness in a sense. Meanwhile, an EXtended Perceptually Shaped Watermarking (EX-
PSW) is acquired through making detection value which approaches to upper limit. It is
proved mathematically that EX-PSW outputs higher detection value than Perceptually
Shaped Watermarking (PSW) with the same distortion constraint. Authors used this idea
and also discussed the adjustment strategies of parameters in EX-PSW, which were
36
helpful for improving the local image quality. Experimental results show that scheme
provides very good results both in terms of image transparency and robustness.
In [10], authors presented an Independent Component Analysis (ICA) [40-41] based
watermarking method. This watermarking scheme is domain-independent ICA-based
approach. This approach can be used on images, music or video to embed either a robust
or fragile watermark. In the case of robust watermarking, the method shows high
information rate and robustness against malicious and non-malicious attacks while
inducing low distortion. Another version of this scheme is a fragile watermarking scheme
which shows high sensitivity to tampering attempts while keeping the requirement for
high information rate and low distortion. The improved performance is achieved by
employing a set of statistically independent sources (the independent components) as the
feature space and principled statistical decoding methods.
In [90], authors presented a dual watermarking Scheme. In general, the watermark
embedding process affects the fidelity of the underlying host signal. Fidelity, robustness
and the amount of data which can be embedded without visible artifacts, often conflict.
Most of early watermarking schemes have focused on embedding the watermark
information applying a global power constraint such as the Peak-Signal-to-Noise-Ratio
(PSNR) to satisfy fidelity constraints. But, the PSNR value is reflecting human’s visual
system because local image properties such as edges or textures are not considered. The
watermarking systems have been proposed that allowed the embedded signal to be locally
varied in response to the local properties of the corresponding host signal [38, 73, 77].
Authors in their paper [90] neglected the PSNR value and use the fact that all common
lossy image compression schemes are PSNR optimized. They embedded watermark
information by geometrically shifting objects and object borders in a given host image. If
an observer has no original image for comparison, the embedding process is
imperceptible. As a consequence, this approach turns out to be extremely robust to
common image compression. Common lossy image compression is optimized for
maintaining the geometric image structure. Hence, as they demonstrate, the embedded
37
information is not affected by a successive embedding approach in the compression
domain.
Authors in their paper [39] presented an improved invariant wavelet and designed a DCT
based blind watermarking algorithm against Rotation-and Scaling-and Translation (RST)
attacks by exploiting the affined invariance of the invariant wavelet. Surviving geometric
attacks in image watermarking is considered to be of great importance. In the face of
geometrical attacks, all shortcomings of almost all digital watermarking algorithms have
been exposed. Therefore, this paper presents an improved invariant wavelet that is better
than the bilinear interpolation and whose performance is close to of bi-cubic when
scaling factor is very close to 1, and designs a novel blind image watermarking algorithm
based on DCT in the (RST) Xiong’s Invariant Wavelet, i.e. RSTXIW domain. The
experiments show that this novel watermarking algorithm is robust against filter, noise
and arbitrary RST geometrical attacks, however, sensitive to local crop attacks.
In their paper [107], authors presented an image watermarking scheme based on 3-D
DCT. A gray-level image is decomposed into a 3-D sub-image sequence by sub sample
of zigzag scanning order that is transformed using block-based 3-D DCT.
Simultaneously, they proved that the distribution of 3-D DCT AC coefficients follows the
generalized Gaussian density function using the distribution relative entropy theory. To
satisfy the balance between the robustness and the imperceptivity, a 3-D HVS model is
improved to adjust the embedding strength. In watermark detecting, the optimum detector
is used to implement the blind detection. It is shown in experiments that the scheme is
strongly robust against various attacks.
In paper [101] proposed digital watermarking scheme uses the properties of DCT and
DWT to achieve almost zero visible distortion in the watermarked images. These
schemes use a unique method for spreading, embedding and extracting the watermark.
Embedding using a linear relation between the transform coefficients of the watermark
and a security matrix has been proposed with satisfactory results.
38
In [59], algorithm is based on multistage Vector Quantization (VQ) that embeds both
robust watermark for copyright protection or ownership verification and fragile
watermark for content authentication or integrity attestation. The method in [122]
combined DCT and VQ to simultaneously embed robust and fragile watermarks.
In their paper [31], two simple dither modulation schemes for a pair of DCT coefficients
are proposed. The first step is to handle the original image using the sub sampling
technique as described in [14]. Then, transform it into DCT domain to obtain four sub
images. By dividing them into two groups, we introduce distinguishing dither modulation
processes in the two coefficient pairs with two robust watermarks. Experimental results
show that the proposed method is blind and robust and through adopting dither
modulation in sub images gained by sub sampling, two independent robust watermarks
can be embedded in the original image.
In the field of color images watermarking, many methods are accomplished by marking
the image luminance, or by processing each color channel separately. Therefore in paper
[55], authors proposed a new DCT domain watermarking expressly devised for RGB
color images based on the diversity technique in communication system. The watermark
is hidden within the data in the same sequence by modifying a subset of the block.
DCT coefficients of each color channel. Detection is based on combination method
which takes into account the information conveyed by three color channels. Even if a
particular channel is severely faded, they are still able to recover a reliable estimated of
transmitted watermark through other propagation channel. Experimental results, as well
as theoretical analysis, are presented to demonstrate the validity of the new approach with
respect to algorithm operating on image luminance only.
2.4 PROBLEM STATEMENT FORMULATION
Since, financial implications of some of the application areas like fingerprinting and
copyright protection are very high and till now no successful algorithm seems to be
39
available to prevent illegal copying of the multimedia contents, the primary goal of this
thesis work is to develop watermarking schemes for images (which are stored in spatial
domain as well as transformed domain) which can sustain the known attacks and various
image manipulation operations. Out of image, audio and video, the image watermarking
was chosen as a goal because any successful image watermarking algorithm may be
extended to video watermarking also. Therefore, to keep the future extension in mind, the
cover medium chosen is an image.
Based on the literature survey presented in Sections 2.1, 2.2 and 2.3, the following issues
were also identified:
ISSUE 1: Till now there in no “Generic” nature in the watermarking algorithms
available. More precisely, if certain approach is applicable for a gray level image, the
same approach does not work for the other formats of an image.
ISSUE 2: Even if gray color image watermarking algorithms are extended for RGB color
images, the maximum work has been done for BLUE color channel only because human
eyes are less sensitive to detect the changes in BLUE color channel. No attack impact
analysis, i.e, which color channel may be affected by a particular attack, has been
carried out.
In view of the above, our problem statements are as follows:
Problem statement 1: Choose Image Watermarking as a major problem.
Problem statement 2: Identify, for multi-color channel images (True color windows
BMP, uncompressed JPEG), the suitability of a color channel with respect to attack (if
any).
Problem statement 3: Explore the ways such that attack impacts may be minimized
before the watermark embedding process.
40
ISSUE 3: In most of the research papers, once the watermarking scheme is finalized, it is
applied to all test images. Since each image is different and has certain characteristics
and after embedding the watermark data by a particular watermarking scheme, its
performance against a particular attack may not be similar with other image. No study is
conducted to make the embedding scheme based on some image characteristics. Thus, the
next problem statement is:
Problem statement 4: Explore the relationship between the performance of
watermarking scheme and the cover image characteristics itself.
ISSUE 4: Mostly watermarking schemes are developed in a way that first a scheme is
developed based on the extension of earlier presented one and then see its performance
against the common image manipulation and known attack. There are huge financial
implications for watermarking schemes (say fingerprinting), but no scheme has been
developed, which is, by design, resistant to at least one attack which can not be
conducted by an attacker, leading to next problem statement:
Problem statement 5: Embed an inherent nature in the developed watermarking
schemes to guarantee that at least one serious attack having most financial implications
cannot attack on watermarked images.
2.4.1 JUSTIFICATIONS OF THE PROBLEM STATEMENT CHOSEN
While deciding the way to start the development of our watermarking schemes, first we
resolved the ISSUE 4, because this must be dealt first among all the 4 issues listed above.
It is known that the application area having the highest financial implications is
‘Fingerprinting”.
If attacker has access to more than one copy of watermarked image, he/she can predict/
remove the watermark data by colluding them. This is known as Collusion attack.
41
Researchers working on “fingerprinting” primarily focus on the “collusion attack”.
Network technology research center, Nanyang Technological University, Singapore
website states that they pay at least equal attention to watermark attacks/counter-attacks
as watermark designs [63]. To facilitate pirate tracing in video distribution applications,
different watermarks carrying distinguishing client information are embedded at source.
If few clients requesting for the same source data get their differently marked versions
together, they may collude to remove or weaken the watermark leading to what is
commonly called “collusion attack”.
Collusion attacks are powerful attacks because they are capable of achieving their
objective without causing much degradation in visual quality of the attacked data
(sometimes, visual quality may even improve after attack.).
In their paper “Multi-bits Fingerprinting for Image” [46], authors focused on collusion
attack for fingerprinting application. It has been stated, that in fingerprinting different
copies for each customer can be produced and this point is very helpful for attackers.
Attackers compare several fingerprinting copies and find the location of the embedded
information and destroy it by altering the values in those places where a difference was
detected.
One more work, specially conducted against collusion attack can be found as “Collusion-
resistant watermarking and fingerprinting (US Patent Issued on June 13, 2006)” [15].
Interested readers can find more literature based on collusion attack on watermarking
system in [127-131].
Therefore, Collusion attack is the most severe problem for the watermarking application
area having the most financial impact. So while designing a watermark scheme, we
decided that our proposed schemes must be designed in such a way that schemes are
inherently collusion attack resistant. Therefore by this thesis, we are presenting a new
term “ICAR (Inherently Collusion Attack Resistant)” as a requirement for a
watermarking system.
42
The other 3 issues were taken into account while developing the watermarking schemes.
After this, we had to decide the working domain and approaches of our developments
based on the findings in the literature survey.
Since transformed domain watermarking has been proved better than spatial domain
watermarking, we decided to start with the transformed domain watermarking for gray
level images and then subsequently move further for Colored and JPEG image
watermarking keeping the first issue in mind.
Apart from ICAR nature and resistant to common image manipulations and known
attacks, we primarily focus the JPEG compression attack. This Lossy attack can reduce
the size of an image up to 1% without altering much visual quality of an image.
Therefore, we picked up the classical Middle Band Coefficient Exchange (MBCE)
scheme (refer Section 2.2.2.1) as a base for developing our schemes because this scheme
takes the JPEG quantization table into consideration to hide the watermark data and thus
ensures the robustness against JPEG compression attack.
To move further, we again had to decide the categories of the watermarking application
areas based on Figure 1.2, we are targeting to develop in this thesis work. Thus,
Figure 2.6 is the same as Figure 1.2 but with highlighted types.
43
Figure 2.6: The Targeted types of to be developed watermarking schemes
The first 2 red highlighters are already justified. The last one (destination based) is again
understood as we are focusing ICAR nature in our watermarking schemes to be
developed, which are highly correlated with fingerprinting which comes under the
destination based watermarking.
Among the visible and invisible, we picked up the non-visible watermarking because in
most of the cases, the presence of the watermark or copyright data is to be hidden. The
most crucial decision before us was to decide the choice among fragile versus robust
watermarking. Since in the business, “Temper detection” have more serious financial
44
implications than the copy or copyright control, we decided to go for fragile
watermarking.
To conclude, it was decided to work on IMAGE WATERMARKING in
TRANSFORMED DOMAIN (more precisely DCT based) to develop an ICAR
watermarking scheme to hide an INVISIBLE watermark data which is FRAGILE in
nature. In addition, the schemes to be developed should be generic in nature i.e. which
could be extended to other images which are stored in spatial domain and transformed
domain.
45
CHPATER-3
PRELIMINARIES
This chapter covers the material which is required to be well understood before coming to
next chapters containing the work contributions. This thesis has given special attention to
JPEG compression of an image because this compression is most commonly used and it
reduces the size of an image very much without noticeable degradation in image quality.
Every image watermarking scheme must survive against JPEG compression attack.
Therefore, first we are giving a brief introduction of JPEG and JPEG2000 image
encoding.
3.1 IMAGE ENCODING STANDARDS 3.1.1 JPEG ENCODING
The JPEG Image Compression is a standard image compression mechanism. Developed
by Joint Photographic Experts Group, JPEG compression is "lossy," because the
compression scheme sacrifices some image quality for a reduction in the image data size.
The JPEG compression scheme [56, 112] is shown in Figure 3.1.
Figure 3.1: JPEG Compression Scheme
First, the source image should be converted from RGB into a different color space called
YCbCr. It has three components Y, Cb and Cr; the Y component represents the
brightness of a pixel, the Cb and Cr components represent the chrominance (split into
46
blue and red components). The YCbCr color space conversion allows greater
compression for the same image quality (or greater image quality for the same
compression). The human eye can see more detail in the Y component (brightness) than
in Cb (blue) and Cr (red). Using this knowledge, encoders can be designed to compress
images more efficiently. The above transformation enables the next step, which is to
reduce the Cb and Cr components (called "downsampling" or "chroma subsampling").
After subsampling, each channel is split into 8×8 blocks (of pixels). Next, each
component (Y, Cb, Cr) of each 8×8 block is converted to a frequency-domain
representation, using two-dimensional DCT.
DCT is a widely used transform coding technique which is able to perform decorrelation
of the input signal in a data independent manner. In case of image, we use 2D DCT. For
more details related to 2D DCT, one may refer pp. 206-220 of “Fundamentals of
Multimedia” [56].
Let us take an example of an 8×8, 8-bit sub image, as shown in Figure 3.2 below:
Figure 3.2: An example sub image
The next step is to transform the subimage from a positive range to the one which is
centered on zero. For an 8-bit image, each pixel has 256 possible values (0 to 255). To
center on zero, it is necessary to subtract each pixel by half the number of possible
values, i.e. 128. Subtracting 128 from each pixel value yields pixel values in the range
[−128,127] resulting in the matrix shown in Figure 3.3.
47
Figure 3.3: Example sub image after subtracting 128 from each pixel
The next step is to take the 2-D DCT which is given by Equation 3.1 below:
Where,
‘u’ is the horizontal spatial frequency, for the integers 0<= u < 8,
‘v’ is the vertical spatial frequency, for the integers 0<= v < 8,
g x,y is the pixel value at coordinates (x, y), and
G u,v is the DCT coefficient at coordinates (u, v).
If we perform this transformation on our matrix above given in Figure 3.3 and then round
to the nearest integer, we get a DCT coefficient matrix, which is shown in Figure 3.4.
Figure 3.4: DCT of sub image shown in Figure 3.3
is a normalizing function,
------------------ (3.1)
48
It may be observed from Figure 3.4, that the top-left corner value is the largest one. This
is the DC coefficient. The remaining 63 coefficients are called the AC coefficients. The
DCT temporarily increases the bit-depth of the image since the DCT coefficients of an 8-
bit/component image take up to 11 or 12 bits (depending on fidelity of the DCT
calculation) to store. This may force the codec to temporarily use 16-bit data to hold these
coefficients doubling the formal size of the image representation at this point. The
advantage of the DCT is its tendency to aggregate most of the signal in one corner of the
result, as may be seen above. The quantization step to follow accentuates this effect while
simultaneously reducing the size of the DCT coefficients to 8 bits or less, resulting in a
signal with a large trailing region containing zeros that the entropy stage can simply
throw away. The temporary increase in size, at this stage, is not a performance concern
for most JPEG implementations because typically only a very small part of the image is
stored in full DCT form at any given time during the encoding or decoding process. After
taking the DCT, next step is the quantization.
The human eye is good at seeing small differences in brightness over a relatively large
area, but at the same time it is not so good at distinguishing the exact strength of a high
frequency brightness variation. This fact allows one to get away with greatly reducing the
amount of information in the high frequency components. This is done by simply
dividing each component in the frequency domain by a constant for that component, and
then rounding to the nearest integer. This is the main lossy operation in the whole
process. As a result of this, it is typically the case that many of the higher frequency
components are rounded to zero, and many of the rest become small positive or negative
numbers which take many fewer bits to store.
A common quantization matrix is shown in Figure 3.5.
49
Figure 3.5: JPEG Quantization matrix
The quantized DCT coefficients are computed with the help of the Equation 3.2 given
below:
where A is the unquantized DCT coefficients; Q is the quantization matrix above; and B
is the quantized DCT coefficients. Using this quantization matrix with the DCT
coefficient matrix in Figure 3.4, DCT values after quantization are given in Figure 3.6.
Figure 3.6: DCT values after quantization
For example,
B11 = round (A1, 1 / Q1, 1) = round (-415/16) = round (-25.9376) = - 26.
It may be noted that most of the AC coefficients are now ZERO. After quantization, as
shown in Figure 3.1, entropy encoding is done as follows:
---------- (3.2)
50
Step-1: Zigzag Scan - The resulting matrix after quantization will contain many zeros.
The lower the quality setting, the more zeros will exist in the matrix. By re-ordering the
matrix from the top-left corner into a 64-element vector in a zig-zag pattern, the matrix is
essentially sorted from low-frequency components to high-frequency components. As the
high-frequency components are the most likely to round to zero, one will typically end up
with a run of zeros at the end of the 64-entry vector. This is important for the next step.
Step-2: DPCM on DC component - On a block-by-block basis, the difference in the
average value (across the entire block, the DC component) is encoded as a change from
the previous block's value. This is known as Differential Pulse Code Modulation.
Step-3: Run Length Encoding (RLE) on AC components - On the individual entries in the
64-element vector (the AC components), a Run Length Encoding stores each value along
with the number of zeros preceding it. As the 1x64 vector contains lot of zeros, it is more
efficient to save the non-zero values and then count the number of zeros between these
non-zero values. The RLE stores a skip and a value, where skip is the number of zeros
before this component, and the value is the next non-zero component.
Step-4: Entropy Coding / Huffman Coding - A dictionary is created which represents
commonly used strings of values with a shorter code. More common strings / patterns use
shorter codes (encoded in only a few bits), while less frequently used strings use longer
codes. So long as the dictionary (Huffman Table) is stored in the file, it is an easy matter
to lookup the encoded bit string to recover the original values.
Once image is stored compressed, it needs to be decompressed for viewing. This scheme
of decompression is given in Figure 3.7.
51
Figure 3.7: JPEG Decompression Scheme
While decompressing, we will multiply the stored values (shown in Figure 3.6) with
quantization matrix. Taking the entry-for-entry product with the quantization matrix
results the matrix shown in Figure 3.8.
Figure 3.8: DCT values regenerated in decompression
Taking the inverse DCT of above values results in an image with values (still shifted
down by 128) gives a matrix shown below in Figure 3.9 (a).
Figure 3.9 (a): Sub image pixel values (still shifted down by 128)
Adding 128 to each entry in the above matrix, we get
52
Figure 3.9 (b): Decompressed sub image pixel values
This is the uncompressed subimage and can be compared to the original subimage (refer
Figure 3.2) by taking the difference (original, uncompressed) results in error values:
Figure 3.10: Error matrix for example sub image
with an average absolute error of about 5 values per pixels as shown below:
Higher compression ratio first affects the high-frequency textures in the upper-left corner
of the image, and contrasting lines become fuzzier. The very high compression ratio
severely affects the quality of the image, although the overall colors and image form is
still recognizable. However, the precision of colors suffers less (for a human eye) than the
precision of contours (based on luminance). This justifies the fact that images should be
first transformed in a color model separating the luminance from the chromatic
information before subsampling the chromatic planes (which may also use lower quality
quantization) in order to preserve the precision of the luminance plane with more
information bits.
For example, an uncompressed 24-bit RGB bitmap image (73,242 pixels) would require
219,726 bytes (excluding all other information headers). The full quality image
53
(Q = 100) is encoded at 9 bits per color pixel, the medium quality image (Q = 25) uses 1
bit per color pixel. For most applications, the quality factor should not go below 0.75 bit
per pixel (Q = 12.5), as demonstrated by the low quality image. The image at the lowest
quality uses only 0.13 bit per pixel and displays very poor color. It could only be usable
after subsampling to a much lower display size.
3.1.2 JPEG2000 ENCODING
JPEG 2000 is a wavelet-based image compression standard [113]. Wavelet transform: In DCT, we use a special cosine based transform. If we carry out
analysis based on both sine and cosine, then a concise notation assembles the results into
a complex valued function of real valued frequency. Such decomposition results in very
fine resolution in frequency domain. However, since a sinusoid is theoretically infinite in
extent in time, such a decomposing gives no temporal resolution. Wavelet transform
represents the signal with good resolution both in time and frequency, by using a set of
besis functions called wavelets. There are two types of wavelet transforms: Complex
Wavelet Transform (CWT) and DWT. Since image is a discrete signal, we are moving to
discrete wavelet transform. Haar is the simplest form of the wavelet transform which
forms the average and difference of the sequence of the values. If we repeatedly take
average and difference and keep results for every step, we create a multi-resolution
analysis of the sequence, as shown in Figure 2.5. For image, this would be equivalent to
creating smaller and smaller summary images, one quarter the size for each step, and
keeping track of differences from the average as well. As shown in Figure 2.5, at each
step image is split in four subbands namely LL (low-low), HL (high-low), LH (low-high)
and HH (high-high). The LL subband can be further decomposed to yield yet another
level of decomposition. More details of DWT and Multiresolution analysis can be found
in [56]. In the core of JPEG2000 is the Embedded Block Coding with Optimized
Truncation (EBCOT) algorithm. The basic idea of EBCOT is the partition of each
subband LL, LH, HL, HH produced by above wavelet transform into small blocks called
code blocks.
54
JPEG 2000 requires more decompression time than JPEG and allows more sophisticated
progressive downloads, yet it averages similar compression rates. Unlike JPEG,
JPEG2000 becomes increasingly blurred with higher compression ratios rather than
generating "blocking and ringing" artifacts. At high bit rates, where artifacts become
nearly imperceptible, JPEG 2000 has a small machine-measured fidelity advantage over
JPEG. At lower bit rates (for example, less than 0.25 bits/pixel for gray-scale images),
JPEG2000 has advantage over certain modes of JPEG in a way that artifacts are less
visible and there is almost no blocking. The compression gains over JPEG are attributed
to the use of DWT and a more sophisticated entropy encoding scheme. JPEG2000
decomposes the image into a multiple resolution representation.
The aim of JPEG2000 is not only improved compression performance over JPEG but
also to add features such as scalability and editability. Very low and very high
compression rates are supported in JPEG2000. To handle a very large range of effective
bit rates is one of the strengths of JPEG2000. For example, to reduce the number of bits
for a picture below a certain amount, the advisable thing to do with the first JPEG
standard is to reduce the resolution of the input image before encoding it. That is
unnecessary when using JPEG 2000, because JPEG2000 already does this automatically
through its multiresolution decomposition structure.
In JPEG2000, images have to be transformed from the RGB color space to another color
space using any of the following two transforms [113]:
1) Irreversible Color Transform (ICT) which uses the well known YCBCR color
space. It is called "irreversible" because of the quantization errors it introduces.
2) Reversible Color Transform (RCT) which uses a modified YUV color space that
does not introduce quantization errors, so it is fully reversible.
After color transformation, the image is split into so-called tiles, the rectangular regions
of the image that are transformed and encoded separately. Tiles can be of any size, and
we may consider the whole image as one single tile but all the tiles will have the same
55
size. Dividing the image into tiles is advantageous because the decoder will need less
memory to decode the image and it can opt to decode only selected tiles to achieve a
partial decoding of the image. The disadvantage of this approach is that the quality of the
picture decreases. Using large number of tiles can create a blocking effect similar to the
older JPEG standard. These tiles are then wavelet transformed to an arbitrary depth, in
contrast to JPEG, which uses an 8×8 block-size DCT. JPEG2000 uses following two
different wavelet transforms:
1) Irreversible: The CDF 9/7 wavelet transform. It is said to be "irreversible"
because it introduces quantization noise that depends on the precision of the
decoder.
2) Reversible: A rounded version of the biorthogonal CDF 5/3 wavelet transform. It
uses only integer coefficients so that the output does not require rounding
(quantization) and therefore, it does not introduce any quantization noise. It is
used in lossless coding.
After the wavelet transform, the coefficients are scalar-quantized to reduce the amount of
bits to represent them at the expense of the loss of quality. The output is a set of integer
numbers which have to be encoded bit-by-bit. The parameter that can be changed to set
the final quality is the quantization step: the greater the step, the greater is the
compression and the loss of quality. With a quantization step that equals 1, no
quantization is performed (it is used in lossless compression).
Above process results in a collection of sub-bands which represent several approximation
scales. A sub-band is a set of coefficients which represent aspects of the image associated
with a certain frequency range as well as a spatial area of the image. The quantized sub-
bands are split further into precincts, the rectangular regions in the wavelet domain. They
are typically selected in a way that the coefficients within them across the sub-bands form
approximately spatial blocks in the (reconstructed) image domain, though this is not a
requirement. Precincts are split further into code blocks. Code blocks are located in a
single sub-band and have equal sizes, except those located at the edges of the image. The
56
encoder has to encode the bits of all quantized coefficients of a code block, starting with
the most significant bits and progressing to less significant bits by a process called
Embedded Block Coding with Optimal Truncation (EBCOT). In this encoding process,
each bit plane of the code block gets encoded in three so-called coding passes, first
encoding bits (and signs) of insignificant coefficients with significant neighbors (i.e. with
1-bit in higher bit planes), then refinement bits of significant coefficients and finally
coefficients without significant neighbors. The three passes are called Significance
Propagation, Magnitude Refinement and Cleanup respectively. Here we are limiting the
discussion of JPEG2000. More details of JPEG2000 can be found in [56, 112-113].
3.2 IMAGE QUALITY MEASURES Through out this thesis, we have used Peak Signal to Noise Ratio (PSNR) and Cross-
Correlation (CC) to measure the quality of the images.
3.2.1 PEAK SIGNAL TO NOISE RATIO
The phrase Peak Signal to Noise Ratio, often abbreviated PSNR, is an engineering term
for the ratio between the maximum possible power of a signal and the power of
corrupting noise that affects the fidelity of its representation. Because many signals have
a very wide dynamic range, PSNR is usually expressed in terms of the logarithmic
decibel scale.
The PSNR is most commonly used as a measure of quality of reconstruction in image
compression etc. It is most easily defined via the Mean Squared Error (MSE). For two
m×n monochrome images I (x, y) and K (x, y), where one of the images is considered a
noisy approximation of the other, MSE is defined as:
--------------------------- (3.3)
57
The PSNR in terms of MSE is defined as:
Here, MAXI is the maximum pixel value of the image. When the pixels are represented
using 8 bits per sample, value of MAXI is 255. More generally, when samples are
represented using linear PCM with B bits per sample, maximum possible value of MAXI
is 2B-1. For color images with three RGB values per pixel, the definition of PSNR is the
same except the MSE is the sum over all squared value differences divided by image size
and by three. Typical values for the PSNR in image compression are between 30 and
40 dB.
3.2.2 CORRELATION COEFFICIENT (CC) The correlation coefficient, a concept from statistics, is a measure of how well trends in
the predicted values follow trends in past actual values. It is a measure of how well the
predicted values, from a forecast model, "fit" with the real-life data. The correlation
coefficient is a number between 0 and 1. If there is no relationship between the predicted
values and the actual values, the correlation coefficient is 0 or very low. As the strength
of the relationship between the predicted values and actual values increases, the value of
correlation coefficient also increases. A perfect fit gives a coefficient of 1.0. Thus the
higher value of correlation coefficient is better. It indicates the strength and direction of a
linear relationship between two random variables.
We can use CC calculation to know the distortion level in our extracted watermark from
an attacked watermarked image. If “A” is the original watermark of size m x n, and “B”
is the extracted watermark, then, in Matlab, we can compute CC using the built-in
function CORR2 ( ) which computes the correlation coefficient ‘r’ as given in
Equation 3.5.
-------------------- (3.4)
58
3.3 TEST DATA For testing the performance of our proposed watermarking schemes for gray and colored
digital images, we are using standard test images available on various test images
databases available on WWW. Figure 3.11 shows the gray level test images of Lena,
Mandrill, Pepper and Barbara. These are gray level images of 256 colors.
Figure 3.11: Test images of Lena, Mandrill, Pepper and Barbara (Courtesy: SPIHT based Image Coding
Incorporating Perceptual Model and Scalability,
http://www.cn.nctu.edutw/faculty/ypl/Students/InChu%20Chen/projrct02/project02.htm)
Figure 3.12 shows the 24 bit true color Windows BMP test images of Lena, Mandrill,
Pepper and goldhill.
----------------------------------------- (3.5)
59
Figure 3.12: Test images of Lena, Mandrill, Pepper and Goldhill
(Courtesy:” Image Processing/Video Codecs/Programming” http://www.hlevkin.com)
Figure 3.13 shows the monochrome watermark logo, used in all proposed watermarking
schemes discussed in this thesis.
Figure 3.13: Watermark logo used in the proposed schemes
61
CHAPTER-4
WATERMARKING OF GRAY IMAGES
4.1 INTRODUCTION
Like most DCT based watermarking schemes, Middle-Band Coefficient Exchange
scheme has proven its robustness against those attacks, which anyhow, do not attack on
the perceptual quality of image (Refer Section 2.2.2.1). For example, JPEG compression
reduces the size of image considerably without having much distortion in visual quality.
Therefore, most of the DCT based schemes are robust against JPEG compression attack.
But in most of the research literature available, even if quality of extracted watermark
logo is good enough to prove the ownership, PSNR value of extracted watermark logo is
less.
In this chapter, we have explained how PSNR value of extracted logo from watermarked
image could be increased if watermarked image has been attacked by JPEG compression
attack. Then we developed a watermarking scheme to increase the robustness against
“Histogram equalization” attack, which attacks on perceptual quality of image.
After developing the watermarking schemes which are robust against JPEG compression
and histogram equalization attack, we developed a watermarking scheme which is
collusion attack resistant by introducing redundancy in Middle Band Coefficient
Exchange scheme. This scheme is not only collusion attack resistant but more robust
against JPEG compression attack as compared to other similar state-of-the-art
watermarking schemes.
62
4.2 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEMES AGAINST JPEG COMPRESSION
Two, classical DCT and DWT based watermarking schemes have already been discussed
in Section 2.2.2.1 and 2.2.3.1. We watermarked the images of Lena, Mandrill and Pepper,
which are shown in Figure 3.11, by applying both the schemes. While watermarking the
chosen images, we used a monochrome logo as a copyright data (or watermark), which is
shown in Figure 3.13. Then, watermarked images, obtained by applying the above said
watermarking schemes, were compressed by JPEG low compression (Quality factor,
Q = 20). From the JPEG compressed images, the watermark data was recovered. As it has
already been mentioned that DCT and DWT based watermarking schemes are robust
against JPEG attack, we found that extracted watermark logo is quite detectible to prove
the ownership as shown in Figure 4.1.
(a)
(b)
Figure 4.1 (a): Extracted watermark logos from test images of Lena, Mandrill and Pepper by applying
DCT based scheme
(b): Extracted watermark logos from test images of Lena, Mandrill and Pepper by applying DWT based
schemes
Though, the extracted watermark logos are quite detectible, we can see the presence of
noise in extracted watermark logos and therefore the PSNR values of extracted
watermark logos are less. Therefore, there is a possibility to further improve the quality
of the extracted watermark logos with an increased PSNR value of extracted watermark
logos.
63
To achieve this, we propose to change the image data or image pixel values such that it
has less impact of JPEG compression attack after getting watermarked without loosing
the perceptual quality to a great extent. We thought to change or modify the image such
that the affect after the attack on the watermarked image could be minimized. We tried to
accomplish this by creating the same effect in an image, before watermarking it, which
this image shall have, after it has been attacked. More precisely, if we know that our
watermarked image may have to suffer JPEG compression attack, whatever changes will
be made by JPEG attack in the watermark image, we tried to incorporate those changes in
the pixel values in advance so that changes caused by JPEG compression attack may be
minimized. This led to the “preprocessing” of the images, i.e., doing some modifications
in the image which are equivalent to the attack before we start watermarking on it, either
by using DCT or DWT based watermarking schemes.
To implement the idea, we decided to analyze the JPEG compression attack on an image
which has been watermarked by DCT and DWT based watermarking schemes. We
proposed three transformation steps before the watermarking of an image, which are as
follows:
1) Take the gray level image which has to be watermarked;
2) Compress it using JPEG scheme; and
3) Convert back the compressed image to gray level image to get the “Transformed
Image”.
We applied the above 3 transformation steps on our chosen test images. First, we
generated 3 “transformed images” of Lena’s test image by keeping the JPEG quality
factor Q = 20, Q = 40 and Q = 60. Then, in the same way, we generated 3 “transformed
images” of remaining 2 test images of Mandrill and Pepper also. Then, we watermarked
“transformed images” as well as “original images”, using both schemes stated above. So,
total 12 images were watermarked separately by DCT as well as DWT based
watermarking schemes. For each of the 3 test images, 4 copies of it were watermarked
where 1 copy was the “original image” and other 3 copies were the “transformed
64
images”, generated by our proposed preprocessing steps. All watermarked images were
then compressed using JPEG low compression (Q = 20). After retrieving the watermark
logos, it was found that the quality of extracted watermark logos from “transformed
images” was better than the quality of extracted watermark logos from “original images”.
Table 4.1 summarizes the PSNR values (in decibel) of extracted watermark logos. It may
be observed that for the test image Lena, PSNR values of extracted logos were better
from all 3 “transformed images” as compared to PSNR value of extracted logo from
“original image” for both DCT as well as DWT based watermarking schemes. But for the
test images Mandrill and Pepper, only 1 “transformed image” generated by keeping
Q = 40, gave the batter PSNR value of extracted logo as compared the PSNR value of
extracted logo from their “original image” for both DCT as well as DWT based
watermarking schemes.
Thus, we conclude that the “preprocessing” for a certain Q enhances the quality of
extracted logos to some extent and, therefore, to increase the robustness of watermarking
schemes against some well known attacks, we must analyze the attack’s characteristics
and its impact on the image and then adjust or preprocess the image in such a manner that
the impact of the attack could be minimized.
4.3 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEME AGAINST HISTOGRAM EQUALIZATION ATTACK
In the previous section, we had discussed about the “preprocessing” of an image to
improve the robustness of DCT and DWT based watermarking schemes against JPEG
compression. We know that transformed domain based watermarking schemes like DCT
and DWT based schemes which were under our consideration in previous section, are
robust against the attacks which do not change the perceptual quality of an image like
JPEG compression attack. We have seen that by our proposed preprocessing, a
watermarked image became more resistant to JPEG compression attack. We decided to
65
see the effectiveness of our proposed idea of “preprocessing” against those attacks which
alter the image perceptually. So, we focused on the “histogram equalization” attack. If we
equalize the histogram of an image, it is affected badly. We would now check whether
our proposed idea of “preprocessing” works in the case of “histogram equalization”?
Table 4.1: PSNR (in decibel) of extracted watermark logo from JPEG compressed (Q = 20) watermarked
image
Results given by
watermarking of
“original image”
Results given by watermarking of “transformed image”.
Imag
e
Sche
me
Use
d
PSN
R o
f Ext
ract
ed
Logo
if O
rigin
al
Imag
e is
not
Tran
sfor
med
PSN
R o
f Ext
ract
ed
Logo
if O
rigin
al
Imag
e is
Tran
sfor
med
usi
ng
Q =
20
PSN
R o
f Ext
ract
ed
Logo
if O
rigin
al
Imag
e is
Tran
sfor
med
at Q
= 40
PS
NR
of E
xtra
cted
Logo
if O
rigin
al
Imag
e is
Tran
sfor
med
at Q
= 60
Lena DCT 31.5694 31.5712 31.5712 31.5742
DWT 30.906 30.924 30.918 30.924
Mandrill DCT 31.6266 31.6254 31.6284 31.6214
DWT 30.888 30.87 30.894 30.888
Pepper DCT 31.5892 31.5878 31.5906 31.5876
DWT 30.598 30.58 30.604 30.598
We watermarked the images of Lena, Pepper, Mandrill and Barbara, which are shown in
Figure 3.11, by applying both DCT and DWT based watermarking schemes. While
watermarking the chosen images, we used a monochrome logo as a copyright data (or
watermark) which is shown in Figure 3.13. Then, for all watermarked images obtained by
applying the above said watermarking schemes, we equalized their histogram and then
recovered the watermark data from the “histogram equalized” images. We found that
extracted watermark logos were quite detectible to prove the ownership, as shown in
Figure 4.2, but all were very noisy. We now “preprocess” the image through the
following proposed 3 transformation steps before watermarking the images:
66
1) Take the gray level image to be watermarked;
2) Adjust the image such that its histogram is equalized to get the “Transformed
Image”; and
3) Apply watermarking DCT and DWT based schemes to the image obtained in step
2.
We applied the above 3 transformation steps on our chosen test images. We generated
“transformed images” of Lena, Pepper, Mandrill and Barbara test image.
For each of the 4 test images, 2 copies of it (1 copy of the “original image” and other
copy of the “transformed images” generated by our proposed preprocessing steps) were
watermarked by DCT and DWT based watermarking schemes. The histograms of all
watermarked images were then equalized. After retrieving the watermark logos, it was
found that the quality of extracted watermark logos from “transformed images” was
better than the quality of extracted watermark logos from “original images”.
(a)
(b)
Figure 4.2 (a): Extracted watermark logos from test images of Lena, Mandrill, Pepper and Barbara by
applying DCT based scheme
(b): Extracted watermark logos from test images of Lena, Mandrill, Pepper and Barbara by applying
DWT based schemes
Table 4.2 summarizes the PSNR values (in decibel) of extracted watermark logos. It may
be observed that, the watermark logos, extracted from watermarked “transformed
67
images” have PSNR values slightly better then the PSNR values of extracted logos
retrieved by watermarked “original image”. Even if, PSNR values were increased
slightly, considerable improvement in perceptual quality was observed. Figure 4.3 shows
the extracted logos from histogram equalized attacked watermarked images. Logos at left
sides are recovered form attacked watermarked “original image”, whereas logos at right
side in the figure are recovered logos form watermarked “transformed images”. We can
easily find that quality of extracted watermark logos from “transformed images” is better
for all the 4 test images.
Table 4.2: PSNR of extracted log from attacked test images
PSNR (DB)
Images
Watermarking
scheme used
PSNR of Extracted
Logos from “Original
Image”
PSNR of Extracted Logos
from “Transformed Image”
DCT 26.43 26.446
Lena DWT 25.79 25.81
DCT 26.378 26.412
Pepper DWT 25.245 25.251
DCT 26.454 26.498
Mandrill DWT 25.887 25.912
DCT 26.122 26.156
Barbara DWT 25.567 25.58
68
Figure 4.3: Extracted logos from “original image” (left) and “transformed image” (right) of Lena,
Mandrill, Pepper and Barbara’s (Top to Bottom) histogram equalized images (By applying DCT based
scheme)
Therefore, we conclude that “preprocessing” the images to minimize the impact of
histogram equalization attack, made the test images more robust against said attack if
DCT and DWT watermarking schemes were used. This favors our statement made in the
previous section that we must analyze the attack’s characteristics and its impact on the
image and then adjust or preprocess the image in such a manner that the impact of the
attack could be minimized.
4.4 DEVISING A COLLUSION ATTACK RESISTANT WATERMARKING SCHEME FOR IMAGES USING DCT
After developing a technique to make DCT and DWT based watermarking schemes
(discussed in Section 2.2.2.1 and 2.2.3.1) more robust against JPEG compression and
histogram equalization attacks, we considered a malicious attack, the ‘collusion attack’
which was discussed in Section 2.4.1. Seeing the financial implications of this attack, we
propose a new term or benchmark for watermarking schemes, the “ICAR” i.e.
69
“Inherently Collusion Attack Resistant”. We recommend that any watermarking
algorithm, by definition, must be “collusion attack resistant” in nature. A watermarking
scheme must be first ICAR and then it should focus on other common image
manipulations and malicious attacks. Henceforth, all watermarking schemes that we are
present are ICAR in nature.
The classical Middile Band Coefficient Exchange (MBCE) scheme , A DCT based
scheme discussed in Section 2.2.2.1, is known to be robust against common image
manipulations and JPEG compression attack. But this scheme, however, cannot sustain
collusion attack. If, any attacker takes more than one copy of a watermarked image, then
by analyzing the patterns of block DCT coefficients, attacker can easily predict the
watermark location and watermark data.
Our aim is to develop an ICAR watermarking scheme which can sustain other common
image manipulations and known attacks also over the existing MBCE scheme which is
not capable of sustaining collusion attack. For developing the new ICAR scheme, the
following 2 issues were kept in mind:
1) If only one pair is used to hide the watermark data, it might happen that by an
attack or by any image manipulation, values of this pair are modified. So, instead
to exchanging only one pair of coefficients from FM region, we should exchange
more than one pair of the coefficient i.e. introduce some redundancy; and
2) To achieve ICAR nature in watermarking scheme, we must ensure that every
copy of watermarked image has a different pattern of hiding watermark data so
that attacker can not conclude the location and content of watermark data even
after analyzing many copies of watermarked image.
Issue no.1 is resolved as follow:
There are 22 coefficients in the FM region in an 8 x 8 DCT block. Out of these 22
coefficients, we can form 17 pairs having nearly the same values in their corresponding
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JPEG quantization table. Therefore, to introduce redundancy in MBCE scheme, we had a
choice to exchange the “n” pairs where the value of “n” ranges from 1 to 11(as there are
total 22 coefficients). We can not disturb or modify all 22 coefficients as it will affect the
image perceptibility. We conducted some experiment on this issue and found that if we
modify the values of 8 coefficients (i.e. 4 pairs are exchanged), no much degradation in
the image perceptibility is recorded. Accordingly, we decided to set the value of “n”
equal to 4.
Issue no. 2 is simply resolved by choosing the combination of 4 pairs randomly in each
watermarked image.
MBCE scheme exchanges 1 pair of coefficient from FM region to hide “1” or “0”. For
example, if coefficients at (3,2) and (2,3) are decided to hide the watermark data, this
scheme sets DCT (3,2) > DCT (2,3) to interpret “1” and set DCT (3,2) < DCT (2,3) to
interpret “0” by exchanging the coefficient values. While decoding the watermark data,
MBCE scheme takes 8 x 8 DCT of watermarked image and by looking the relative
strength of the coefficients at these locations, it decodes the “1” or “0” to reconstruct the
watermark data. The proposed ICAR scheme exchanges 4 pairs which indicates that
either “0” or “1” is hidden in the block.
One such combination of 4 pairs may be taken as:
{(5,1) and (4,2), (6,3) and (5,4), (5,2) and (4,3), (3,2) and (2,3)}.
A scheme is robust if it is able to recover watermark data even if most of the middle band
conefficients are attacked. To achieve this, we need to develop some dependencies on
low frequecny coefficients also. In Figure 2.3 and Figure 2.4, values present at location
(0,1) and (1,0) in 8x8 block DCT are low frequency coefficients of an image and attacker
can not change the values at these locations because it will affect the image badly.
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Figure 4.4: Swapping of 4 pairs to hide “0” or “1” in conjunction with low frequency values
We developed a scheme of exchanging 4 middle-band coefficient pairs in strong
correlation with low frequency coefficients such that even if attacker successfully
attacks on 3 pairs, only 1 pair of coefficient will decode the watermark data correctly.
Swapping criteria of the proposed scheme is illustrated in Figure 4.4.
More details of encoding and decoding process are given in Section 4.4.2 and 4.4.3.
The proposed watermarking scheme can be defined as a 7-tuple (X, W, P, K, G, E, D),
where
1) X denotes the set of instances Xi, of a particular gray level image, (If N copies of
an image are to be watermarked, then 0 ≤ i ≤ N);
2) W denotes the monochrome watermark logo;
3) P denotes the set of policies Pi, 0 ≤ i ≤ N;
4) K denotes the watermark strength parameter;
5) G denotes the policy generator algorithm G: Xi Pi, where each Xi will have a
unique Pi, i.e. a different policy to hide the watermark data;
6) E denotes the watermark embedding algorithm, E: Xi x W x Pi Xi’;
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7) D denotes the watermark detection algorithm, D: Xi’ x Pi W’, where W’
represents extracted watermark.
Out of these 7 tuples, last 3 tuples are algorithms as discussed below:
4.4.1 G, THE POLICY GENERATOR ALGORITHM
To watermark each copy Xi of an image X differently, we need a different watermarking
policy. Here “Policy” means that for every copy of the image, there will be unique
combination of 4 pairs of middle band coefficients. To generate a policy, we simply take
8 x 8 DCT of the input image Xi and randomly select 4 pairs out of 17 pairs of middle
band region. So, number of policies that can be generated are 17C 4 = 2380 which means
that 2380 copies of a single image can be watermarked such that no two watermarked
images have same policy. This step ensures that attacker can not conclude the location of
watermark data by colluding many watermarked copies of an image. This also depicts
that our proposed scheme is an ICAR scheme.
4.4.2 E, THE WATERMARK EMBEDDING ALGORITHM
In this algorithm, each 8 x 8 DCT block of an image is used to hide a single bit of
watermark logo. This algorithm is given as below:
1. Repeat steps 2 to 13 for i = 1…..n;
// where ‘n’ is the number of copies of a single image to be watermarked //
2. INPUT (Xi);
3. Take 8 x 8 block DCT of Xi;
4. INPUT (W);
5. Convert W into a string S = (Sj | Sj = {0, 1}, for j = 1…..length of the watermark);
6. Let L = STRING_LENGTH (S);
// where L is the length of watermark data. If L=1000, then first 1000 DCT block of Xi are used //
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7. Pi = CALL (G);
// Each Pi shall be stored in an author’s database for the detection purpose in future. Let the Pi,
for chosen Xi, be {(5,2) and (4,3), (6,3) and (5,4), (5,1) and (4,2), (3,2) and (2,3)} //
8. Repeat steps 9 to12 for r = 1…..L;
9. Read Sr;
10. If Sr = 0
If (DCT (0, 1) > DCT (1, 0))
Swap the DCT coefficients from chosen Pi such that coefficients at
(5,2), (6,3), (5,1) and (3,2) become larger than (4,3), (5,4), (4,2)
and (2,3) respectively;
If (DCT (0, 1) <= DCT (1, 0))
Swap the DCT coefficients from chosen Pi such that coefficients at
(5,2), (6,3), (5,1) and (3,2) become smaller than (4,3), (5,4), (4,2)
and (2,3) respectively;
Else If Sr =1
If (DCT (0, 1) <= DCT (1, 0))
Swap the DCT coefficients from chosen Pi such that coefficients at
(5,2), (6,3), (5,1) and (3,2) become larger than (4,3), (5,4) ,(4,2)
and (2,3) respectively;
If (DCT (0, 1) > DCT (1, 0))
Swap the DCT coefficients from chosen Pi such that coefficients at
(5,2), (6,3), (5,1) and (3,2) become smaller than (4,3), (5,4), (4,2)
and (2,3) respectively;
End;
11. For all swapped coefficients pairs repeat the step 12;
12. If (DCT (u1, v1) – DCT (u2, v2) > K)
If (DCT (u1, v1) > DCT (u2, v2))
DCT (u1, v1) = DCT (u1, v1) + K/2;
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DCT (u2, v2) = DCT (u2, v2) - K/2;
Else
DCT (u1, v1) = DCT (u1, v1) - K/2;
DCT (u2, v2) = DCT (u2, v2) + K/2;
End;
// Like Classical MBCE scheme (Section 2.2.2.1), robustness of the watermark can be improved
by using a watermark “strength” constant K such that for all 4 chosen pairs,
DCT (u1, v1) – DCT (u2, v2) > K. If coefficients do not meet these criteria, they should be
modified by using some random noise to satisfy the relation. Increasing K thus reduces the
chance of detection errors at the expense of additional image degradation. This ensures that
larger coefficients remains larger even after image manipulations because coefficients relative
values will decide the decoding of the watermark data //
13. Take IDCT to reconstruct Xi;
14. End.
4.4.3 D, THE WATERMARK DETECTION ALGORITHM
We decode “1” and “0” based on the swapping criteria shown in Figure 4.4. The
detection algorithm steps are as follows:
1. INPUT (Xi’);
// Xi’ is the attacked copy of a watermarked image //
2. Take 8 x 8 block DCT of Xi’;
3. For each Pi in author’s database, repeat the steps 4;
// If initially 10 copies were watermarked, then out of 10 policies, for 1 policy, watermark will be
recovered correctly. To explain further steps, we are assuming that now algorithm is in a loop
where Pi is {(5,2) and (4,3), (6,3) and (5,4), (5,1) and (4,2), (3,2) and (2,3)}, which was used to
watermark this particular Xi’ //
4. Repeat the steps 5 for j = 1….L;
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// L is the length of watermark data. A single bit will be recovered form one 8x8 DCT block //
5. Take jth DCT block to form jth bit of watermark as follows:
If (DCT (1, 2) > DCT (2, 1))
If (DCT (5, 2) > DCT (4, 3))
T1 = 1; else T1 = 0;
If (DCT (5, 1) > DCT (4, 2))
T2 = 1; else T2 = 0;
If (DCT (6, 3) > DCT (5, 4))
T3 = 1; else T3 = 0;
If (DCT (3, 2) > DCT (2, 3))
T4 = 1; else T4 = 0;
If (T1 + T2 + T3 + T4 > 1)
Decode “0”;
Else decode “1”;
Else If (DCT (1, 2) <= DCT (2, 1))
If (DCT (5, 2) > DCT (4, 3))
P1 = 1; else P1 = 0;
If (DCT (5, 1) > DCT (4, 2))
P2 =1; else P2 = 0;
If (DCT (6, 3) > DCT (5, 4))
P3 = 1; else P3 = 0;
If (DCT (3, 2) > DCT (2, 3))
P4 = 1; else P4 = 0;
If (P1 + P2 + P3 + P4 > 1)
Decode “1”;
Else decode “0”;
End;
6. Store W’, the recovered watermark;
7. End.
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Even if three pairs are attacked to confuse the decoder, only one pair in conjunction with
the relationship between DCT (1, 2) and DCT (2, 1), enables us the detection of “1” or
“0”. That is why the line (T1 + T2 + T3 + T4 > 1) is written. If there is no change in
watermarked image, all values will remain unaffected and we can set the condition
(T1 + T2 + T3 + T4 > 3).
4.4.4 PERFORMANCE OF THE PROPOSED SCHEME
To incorporate the ICAR nature, we have introduced redundancy and randomness in
classical MBCE scheme. Because of this attacker has no mechanism to conduct pattern
analysis to find out the location of the watermark data. Therefore we can say that the
proposed scheme’s design ensures that pattern analysis by colluding many watermarked
copies is not possible and thus the scheme is ICAR. Now, in order to check that injecting
the ICAR nature in the scheme did not degrade the performance against common image
manipulations and known attacks, we tested our scheme on 3 well known test images of
Lena, Mandrill and Pepper of size 512 x 512 and 256 colors in Windows BMP format as
shown in Figure 3.11. We generated the watermarked copies at various watermark
strength constant K. Values of K were chosen from 10 to 50, and then for all
watermarked copies, watermark logos were recovered. Obviously, for higher values of K,
the quality of extracted watermark logos were fine but the quality of watermarked image
itself, was affected much. On the other hand, for the lower values of K, the watermarked
image generated were of finer quality but the quality of extracted watermark logos from
such images was poor. This is an obvious “Imperceptibility versus Robustness” trade-off.
It was observed that, the value K = 20 was the best value under the circumstances. For
this value of K, the recovery was good without losing much image quality. So, further
tests were conducted by using K = 20.
4.4.4.1 PERFORMANCE AGAINST JPEG COMPRESSION: All watermarked test
images were compressed using JPEG scheme at various JPEG quality factors. Even with
quality factor, Q = 20 (9.1 % of original size, JPEG Low compression), extracted logos
were quite detectible. Table 4.3 summarizes the PSNR values of extracted watermark
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logos from JPEG compressed watermarked images. Figure 4.5 shows the extracted
watermark logos from JPEG compressed watermarked test images. It may be observed
from both the specified table and the figure that our proposed scheme is capable of
sustaining JPEG compression attack and even at Q = 20, the recovery of the watermark
logo is quite efficient.
4.4.4.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS: All
watermarked test images were then tested against Horizontal flip, Scaling,
Brightness / Contrast (both - 20 to + 20) adjustment and Noising. Our scheme sustained
all above image manipulations. Figure 4.6 shows the extracted watermark logos
recovered by the test image of Lena, which had undergone all the above stated attacks.
Same results were found for other 2 test images also.
Table 4.3: PSNR of extracted watermarks after JPEG compression
PSNR (DB)
Quality
factor
Lena Watermarked
Image
Mandrill
Watermarked Image
Pepper
Watermarked Image
80 23.724 23.736 23.73
60 23.715 23.7315 23.724
40 23.6955 23.7285 23.706
30 23.697 23.724 23.7075
20 23.6775 23.7195 23.6925
Figure 4.5: Extracted watermark logos after JPEG compression at Q = 20 from watermarked Lena,
Mandrill and Pepper images
4.4.4.3 COMPARATIVE STUDY WITH OTHER MECHANISMS: We compared
the performance of the proposed scheme for the JPEG compression with other similar
78
state-of-the-art methodologies which are well known for their robustness against JPEG
compressions. Schemes chosen were as follows:
Scheme-A: Correlation based Schemes with 1 PN sequence (Section 2.1.3.1)
Scheme-B: Correlation based Schemes with 2 PN sequence (Section 2.1.3.2)
Scheme-C: DCT Domain based Scheme (Section 2.2.2.1)
Scheme-D: DWT Based Scheme. (Section 2.2.3.1)
Watermarked images, obtained by proposed scheme as well as by other four schemes
(Scheme-A to Scheme-D) were then compressed at various JPEG quality factors. We
named our proposed scheme as Scheme-E. As all the above said watermarking schemes
were robust against the JPEG compression attack, we evaluated them at different scale.
All schemes were evaluated for how rapidly the scheme would start losing its robustness
as the JPEG quality factors goes down. It was observed that up to Q = 40, performance of
all watermarking schemes were approximately equal but for lower values of JPEG quality
factor (Q < 40), our scheme showed more resistant as compared to scheme-A and
scheme-B. The percentage decrease in quality of extracted watermark with respect to
JPEG quality factors were compared as shown in Figure 4.7. It may be observed that
performance of proposed scheme is better then Scheme A and Scheme B for low JPEG
compression. Proposed scheme loses its performance as compared to DCT and DWT
based schemes because we are increasing robustness against collusion attack (by making
it ICAR) at the expanse of quality (by introducing redundancy).
Figure 4.6: Extracted watermark logos from Lena’s image after Horizontal flipped, scaled,
brightness /contrast adjusted and Noising (Left to Right, Top to bottom)
79
Figure 4.7: Percentage decrease in quality of extracted watermark with respect to JPEG quality factor
So, even after introducing redundancy in classical MBCS scheme to fight against
collusion attack, quality of recovered watermark does not decrease very much as
compared to Scheme-C and Scheme-D and better than Scheme-A and Scheme-B. We,
therefore, conclude that our proposed ICAR watermarking scheme is quite robust against
JPEG compression and common image manipulations for watermarking of gray BMP
images.
4.5 CONCLUSION
To summarize this chapter, we can say that if DCT and DWT based watermarking
schemes discussed in Section 2.2.2.1 and 2.2.3.1 are to be used for the watermarking of a
gray BMP image, then the image becomes more resistant to JPEG compression attack if
we transform the original image to JPEG image at certain JPEG quality factor and then
convert it back to gray level image. Similarly, if we preprocess the image in such a way
that its histogram is equalized, then also an image become more resistant to histogram
equalization attack for the same watermarking schemes. So, a modification in the image
such that the affect after the attack on the watermarked image could be minimized,
increases the robustness of schemes for DCT and DWT based watermarking schemes.
Then, we developed a DCT based ICAR watermarking scheme which was very robust
against JPEG compression attack and other common image manipulations.
0 20
40
60
80
100
120
Q80 Q60 Q40 Q30 Q20
Schema-ASchema-B
Schema-CSchema-DSchema-E
80
81
CHAPTER-5
WATERMARKING OF COLOR IMAGES
5.1 INTRODUCTION After satisfactorily developing the watermarking schemes for gray level images, we
focused on developing the watermarking schemes for the color images. The proposed
ICAR watermarking scheme given in the previous chapter was chosen as a base as it has
already proved its resistance to JPEG compression attack and other common image
manipulations and performed at par with other state-of-the-art watermarking schemes. In
this chapter, we conducted a study to find out the suitability of color channel
(Red/Green/Blue) to hide the watermark data while using the DCT based watermarking
scheme. We present an ICAR watermarking schemes for true colored BMP images.
5.2 PERFORMANCE ANALYSIS OF COLOR CHANNEL FOR DCT BASED IMAGE WATERMARKING SCHEME
Initially, the suitability of color channel to hide a monochromatic watermark in a 24-bit
color Window’s BMP image while using classical MBCE watermarking scheme, was
examined as MBCE scheme is the base scheme used to develop the proposed ICAR
watermarking schemes.
Four well known 24 bit colored test images of Lena, Pepper, Mandrill and Monarch
(Size 512 x 512 pixels), shown in Figure 3.12 were taken. The watermark logo used is
shown in Figure 3.13. Then, the watermark logo was embedded in these 4 test images
using the MBCE watermarking scheme. To analyze the performance of Red, Green and
Blue channels, the watermark was embedded separately in R, G and B channels one by
one. So, total 4 images were watermarked and each of these images ware watermarked
thrice.
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Table 5.1: PSNR of Extracted watermark from JPEG compressed watermark test images
RED (PSNR-5.86) GREEN(PSNR -10.86) BLUE(PSNR -4.45)
Figure 5.1: Recovered watermarks for Lena.bmp after jpeg attack at Q = 40
After watermarking the test images in all three color channels, we compressed all 12
watermarked images using JPEG compression at 4 JPEG quality factors (Q = 80, 60, 40,
and 20) and then recovered the watermark logos from JPEG compressed images. We
calculated the PSNR values of all these 12 x 4 = 48 extracted watermark logos. Table 5.1
summarizes their PSNR values. The recovered watermark logos from all 3 Lena’s test
images, if they were JPEG compressed at Q = 40, are shown in Figure 5.1. It was
observed that for all test images, quality of extracted watermark logo was better, if
watermark is embedded in GREEN channel for all JPEG quality factors. This can be
justified as follows:
JPEG uses the YCbCr color model. While converting from BMP to JPEG, following
color transformation occurs:
Y’ = 0.299 x R +0.587 x G + 0.114 x B
Cb = 128 - 0.168 x R - 0.331 x G + 0.500 x B
Cr = 128 + 0.500 x R - 0.419 x G - 0.081 x B
LENA.BMP PEPPER.BMP
JPEG Compression Q = 20 Q = 40 Q = 60 Q = 80 Q = 20 Q = 40 Q = 60 Q = 80
RED 3.8138 5.86586 9.38713 13.737 4.44446 6.12934 11.3841 14.9055
GREEN 6.2285 10.8642 14.1634 17.8173 6.48408 10.3018 13.9941 15.7327
BLUE 3.78458 4.45904 7.00983 14.0586 4.0144 4.59762 7.27842 13.686
MANDRILL.BMP MONARCH.BMP
JPEG Compression Q = 20 Q = 40 Q = 60 Q = 80 Q = 20 Q = 40 Q = 60 Q = 80
RED 4.58561 6.09133 13.5342 17.9727 4.18767 6.06572 10.7443 15.4762
GREEN 7.3024 14.3188 18.4186 20.1702 4.88113 10.393 13.274 16.1883
BLUE 4.00899 4.72608 11.0469 17.8341 3.78916 4.38603 7.24002 13.9242
--------- (5.1)
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Where Y' is the luminance component and Cb and Cr are the blue and red chrominance
components. Y'CbCr is not an absolute color space. It is a way of encoding RGB
information and the actual color displayed depends on the actual RGB colorants used to
display the signal. It is clear from Equations 5.1 that G is multiplied by relatively larger
coefficient and thus green channel should carry the watermark data for the better
recovery if images are JPEG compressed after the watermarking using the MBCE
scheme.
Now to further validate the concept of ‘preprocessing’ introduced in previous chapter,
color channels of all test images were histogram equalized one at a time, i.e., Lena image
had now 3 copies where in one copy only red channel is equalized, in another copy only
green channel is equalized and in the third copy only blue channel is equalized leading to
12 test images to be watermarked. The watermark logo was embedded in the histogram
equalized color channel for all 12 test images. We performed the following attacks on the
watermarked images:
1) JPEG Attack (low JPEG compression with Q = 20);
2) Noise Attack (adding 10% Gaussian noise in the watermarked images); and
3) Histogram Equalization (equalizing the histogram of the watermarked images).
The watermark logos were recovered from the attacked images and their PSNR values
were calculated. Table 5.2 summarizes the PSNR values of watermark logos recovered. It
may be observed from Table 5.2 that for all cases if a color channel of the image was
HISTOGRAM EQUALIZED before embedding the watermark, recovery of watermark is
better i.e. PSNR values are higher. Therefore, our proposed idea of ‘preprocessing’
worked well for colored BMP images also. It may be further observed that the difference
in the PSNR values of recovered logos from original image and equalized image are high
in the case of “histogram equalization” attack because our preprocessing step is itself the
histogram equalization. These results further prove that a modification in the image such
that the effect after the attack on the watermarked image could be minimized, increases
the robustness against that attack for colored images watermarking algorithm.
84
It is, therefore, concluded that to decide the color channel to carry the watermark data, we
will have to analyze the characteristics of attack itself. If there is high probability that
watermark image may undergo JPEG compression, we should select the GREEN channel
because while converting to JPEG format, green channel’s data has higher impact as
compared to other color channel’s data.
Table 5.2: PSNR of extracted watermark from attacked watermarked test images
LENA.BMP PEPPER.BMP
Color
Channel
Attack
Jpeg
Q20
His
togr
am
Equ
aliz
ati
on
Noi
se
(12.
5%)
Jpeg
Q20
His
togr
am
Equ
aliz
ati
on
Noi
se
(12.
5%)
Original 3.85853 15.5867 4.9277 3.89485 17.4944 6.87319
RED Equalized 4.7334 15.6074 5.13784 4.706 19.4657 7.05068
Original 6.2285 15.7285 5.25512 4.50915 14.7932 6.72312
GREEN Equalized 6.8358 18.7032 5.37656 6.9542 16.9559 7.69791
Original 3.78205 16.8769 4.96932 3.70676 15.503 6.7605
BLUE Equalized 4.1447 23.0387 5.41004 4.1985 18.6343 8.12838
MANDRILL.BMP MONARCH.BMP
Color
Channel
Attack
Jpeg
Q20
His
togr
am
Equ
aliz
ati
on
Noi
se
(12.
5%)
Jpeg
Q20
His
togr
am
Equ
aliz
ati
on
Noi
se
(12.
5%)
Original 4.58561 17.1942 8.9886 4.18767 16.9726 7.712
RED Equalized 5.2266 16.7228 9.36638 4.7169 18.1131 7.86498
Original 7.3024 17.4885 9.2963 4.88113 16.3554 7.65961
GREEN Equalized 11.1118 21.6698 9.81065 6.9542 21.0406 8.14772
Original 4.00899 16.7143 8.75787 3.78916 14.483 7.48674
BLUE Equalized 4.3586 18.3073 8.86063 4.3045 20.213 8.39899
85
It is also clear from Table 5.2 that for attacks other than JPEG Compression, performance
of color channels for all images had no fixed pattern which means that robustness may
depend upon the attack characteristics as well as “image characteristics” also.
Therefore, the goal for the further development was not only to develop an ICAR
watermarking scheme but also to find out some relationship between the performances of
our proposed schemes with the “image characteristics” itself.
5.3 DEVISING AN ICAR WATERMARKING SCHEME FOR COLORED BMP IMAGES
In the previous chapter, we have proposed an ICAR scheme for watermarking gray level
image. Results indicated that this scheme was not only an ICAR scheme but also very
robust to JPEG compression attack and other common image manipulations. Therefore,
we decided to extend the same approach for colored BMP images also. In the earlier
proposed ICAR scheme, we have introduced redundancy in swapping and made the
swapping criterion dependent on low frequency coefficient. To further improve the
robustness, we propose a new swapping criterion with the assurance that no two
watermarked copies of an image have same policy of watermarking. An attacker may
attack on large number of middle band coefficients but if image has to remain
perceptually unchanged, the average value (Av) of all middle band coefficients (total 22
in numbers) will not modify to a great extent. So, unlike the previous scheme where we
swapped 4 pairs, we swapped 4 middle band coefficients (not pair) with the “Av” value.
Details of this swapping mechanism are described in Section 5.3.3.
The proposed watermarking scheme can be defined as a 7-tuple (X, W, P, T, G, E, D)
where:
1) X denotes the set of instances Xi of a particular gray level image, (If N copies of
an image are to be watermarked, then 0 ≤ i ≤ N);
2) W denotes the monochrome watermark logo;
3) P denotes the set of policies Pi, 0 ≤ i ≤ N;
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4) “T” is the “watermark strength parameter”;
5) G denotes the policy generator algorithm G: Xi Pi, where each Xi will have a
unique Pi, i.e. a different policy to hide the watermark data.
6) E denotes the watermark embedding algorithm, E: Xi x W x Pi Xi’;
7) D denotes the watermark detection algorithm, D: Xi’ x Pi W’, where W’
represents extracted watermark.
The parameter “T” is analogous to “K” of classical MBCE scheme. In classical MBCE
scheme, relative strength of 2 coefficient’s value of FM region decides the decoding of
“1” or “0”. If the relative strength of 2 values has to decide the decoding of “0” or “1”,
then larger value should remain larger even after image manipulations. So, we adjust
these values in such a way that the difference between the 2 values becomes larger than a
certain threshold value. We name this threshold value as “Watermark Strength
Parameter” because this value decides the robustness of watermark data. Certainly, it has
an impact on the image perceptibly. So, we have to decide this threshold value in such a
way that our image does not loose its quality much.
Out of these 7 tuples, last 3 tuples are algorithms which are discussed below:
5.3.1 G, THE POLICY GENERATOR ALGORITHM
Similar to our earlier proposed ICAR watermarking scheme for the gray image
watermarking, we had to watermark each copy Xi of an image X differently. Therefore,
we need a different watermarking policy for each copy of the image to be watermarked.
Here “Policy” means that for every copy of the image, there will be unique combination
of 4 middle band coefficients. To generate a policy, we simply take 8 x 8 DCT of the
input image Xi and randomly select 4 coefficients out of 22 middle band coefficient of
FM region from any of the red, green or blue color channel. So, numbers of policies that
can be generated are 22C 4 = 7315 which means that 7315 copies of a single image can be
watermarked such that no two watermarked images have same policy. This step ensures
that attacker can not conclude the location of watermark data by colluding many
87
watermarked copies of an image. This also depicts that our proposed scheme is an ICAR
scheme.
5.3.2 COLOR CHANNEL SELECTION
Up to the development of this approach, we used only “BLUE” color channel to hide the
watermark data. Bossen et al. [9] have stated that the watermarks should be embedded
mainly in the BLUE color channel of an image. The human eye is least sensitive to
change in BLUE channel. However, the suitability of color channel to hide the watermark
data is dependent on the image itself and therefore, we have discussed some interesting
results related to this issue in the Chapter 6. In this section, we are using BLUE channel
to hide the watermark data.
5.3.3 E, THE WATERMARK EMBEDDING ALGORITHM
In this algorithm, each 8x8 DCT block of an image is used to hide a single bit of
watermark logo. Our embedding algorithm is based on averaging the coefficients of FM
region. We can fight against collusion attack by swapping more than one pair but if
attacker is ready to loose some quality, he/she can disturb all the coefficients in FM
region. Therefore, even if we introduce redundancy with randomness, our watermark data
may still be attacked. So, we propose that an attacker cannot alter the image such that the
“average” of coefficients of FM region changes much. Accordingly, we are hiding “1” or
“0” by using relative value of a coefficients and the average “Av” of coefficients of FM
region. This algorithm is given as below:
1. Repeat steps 2 to 11 for i = 1…..n;
// where ‘n’ is the number of copies of a single image to be watermarked //
2. INPUT (Xi);
3. Take 8x8 block DCT of Xi;
4. INPUT (W);
5. Convert W into a string S = (Sj | Sj = {0,1}, for j = 1…..length of the watermark);
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6. Let L = STRING_LENGTH (S); // L is the length of watermark data. If L = 1000, then first 1000 DCT block of Xi are used //
7. Pi = CALL (G); // Each generated Pi shall be stored in an author’s database for the detection purpose in future.
Let the Pi for chosen Xi be, Pi = {(5,1), (4,2), (6,3) and (5,4)} in BLUE channel //
8. Calculate the average “Av” of remaining 18 middle band coefficients.
9. Repeat steps 10 to11 for r = 1…..L;
10. Read Sr;
// Now like classical MBCE scheme, relative strength of average “Av” and chosen 4 coefficients
in step 7 will interpret “0” or “1” of watermark data. To hide “0”, for all 4 chosen coefficients
in step 7, we assigned the value of coefficients which is ‘T’ less than the average “Av”. To hide
“1”, for all 4 chosen coefficients in step 7, we assigned the value of coefficients which is ‘T’
greater than the average “Av” //
If (Sr = 0)
DCT (5, 1) = Av - T;
DCT (4, 2) = Av - T;
DCT (5, 4) = Av - T;
DCT (6, 3) = Av - T;
Else
DCT (5, 1) = Av + T;
DCT (4, 2) = Av + T;
DCT (5, 4) = Av + T;
DCT (6, 3) = Av + T;
End;
11. Take IDCT to reconstruct Xi;
12. End.
5.3.4 D, THE WATERMARK DETECTION ALGORITHM Watermark extraction is the reverse procedure of watermark embedding. To extract the
watermark from the watermarked image, we calculated the average “Av” in the same way
as in embedding algorithm. Owner should have a record of all policies used to watermark
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the image. Based on “policies”, owner of the image can recover watermark using
following rules:
1) If at least 1 out of 4 chosen coefficients are less than average, Interpret “0”; and
2) If at least 1 out of 4 chosen coefficients are greater than average, interpret “1”.
The detection algorithm steps are as follows:
1. INPUT (Xi’); // Xi’ is the attacked copy of a watermarked image //
2. Take 8x8 block DCT of Xi’ and calculate Av;
3. For all Pi in author’s database, repeat the steps 4; // If initially 10 copies were watermarked, then out of 10 policies, for 1 policy, watermark will be
recovered correctly. To explain further steps, we are assuming that now algorithm is in a loop
where Pi is {(5,1) (4,2) (5,4) and ( 6,3)}, which was used to watermarked this particular Xi’ //
4. Repeat the steps 5 for j = 1….L; // L is the length of watermark data. A single bit will be recovered form one 8x8 DCT block//
5. Take jth DCT block to form jth bit of watermark as follows:
If (DCT (5, 1) <= Av)
T1 = 1;
Else T1 = 0;
If (DCT (4, 2) <= Av)
T2 = 1;
Else T2 = 0;
If (DCT (5, 4) <= Av)
T3 = 1;
Else T3 = 0;
If (DCT (6, 3) <= Av)
T4 = 1;
Else T4 = 0;
If ( T1 + T2 + T3 + T4 >= 1)
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Decode “0”
If (DCT (5, 1) > Av)
P1 = 1;
Else P1 = 0;
If (DCT (4, 2) > Av)
P2 = 1;
Else P2 = 0;
If (DCT (5, 4) > Av)
P3 = 1;
Else P3 = 0;
If (DCT (6, 3) > Av)
P4 = 1;
Else P4 = 0;
If ( P1 + P2 + P3 + P4 >= 1)
Decode “1”
End;
6. Store W’, the recovered watermark;
7. End.
It may be observed from both the algorithms that even if attacker alters the values of the
coefficient of FM region, if “Av” is not changed much, then we can recover the
watermark data correctly and attacker cannot aim to attack the image in such a manner
which modifies “Av”.
5.3.5 PERFORMANCE OF THE PROPOSED SCHEME
Our proposed scheme does not need any testing to check whether or not it is robust
against the collusion attack, as it is designed in such a way that the attacker can not
analyze the pattern by colluding many watermarked copies. We needed to check the
performance of the proposed scheme against the JPEG compression and other common
image manipulations and known attacks. For this, we tested our scheme on 3 test images
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Lena, Mandrill and Pepper of size 512 x 512 in Windows 24 bit BMP format, shown in
Figure 3.12.
Firstly, we chose an appropriate value of “T” which affects least the image quality as well
as optimizes the recovery of watermark data. Our experiments suggested that if we were
hiding the watermark using T = 150, there was approximately no loss in the perceptual
quality of the images and recovered watermark logos were of very fine quality.
Figure 5.2 shows the watermarked test images after hiding watermark logo by keeping
T = 150. It may be seen that, images are not disturbed at all. Figure 5.3 shows the
extracted watermark logos from these watermarked copies of Lena, Mandrill and Pepper
without performing any attack or manipulations on the watermarked images. This fixed
up the value of T = 150 for further tests.
Figure 5.2: Watermarked test images keeping T = 150
Figure 5.3: Extracted watermark from watermarked Lena, Mandrill and Pepper images respectively at
T = 150
5.3.5.1 PERFORMANCE AGAINST JPEG COMPRESSION: We applied JPEG
compression on watermarked images (generated by keeping T = 150) at different JPEG
quality parameters Q and then recovered the watermark logos. Table 5.3 summarizes the
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PSNR of extracted watermark logos. It may be observed from Table 5.3 that even at
Q = 20, quality of extracted watermark is very fine and logos are quite detectible.
5.3.5.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS: We
performed the following attacks on the watermarked test images:
Attack-1: Equalize the Histogram;
Attack-2: Apply uniform scaling (Zoom);
Attack-3: Adjust the brightness to +40 and contrast to +25;
Attack-4: Adjust the hue and saturation to +10 each;
Attack-5: Add 10 % Gaussian noise; and
Attack-6: Blur the image using Gaussian blur with 1 pixel radius.
Table 5.3: PSNR of extracted watermark logos after JPEG compression
PSNR (DB)
Quality
factor
Lena
Watermarked
with T = 150
Mandrill
Watermarked
with T = 150
Pepper
Watermarked
with T = 150
Q = 80 39.9987 37.0185 39.9987
Q = 60 39.9987 34.98135 39.9987
Q = 40 24.57225 14.51025 25.20285
Q = 20 21.92385 12.26715 21.3678
Then, we recovered the watermark logos from attacked images and calculated the PSNR
value of watermark logos. Table 5.4 summarizes the PSNR values of extracted logos
recovered from all test images. Our proposed scheme sustained all the attacks and the
quality of the extracted watermark logos is quite good. Figure 5.4 shows the recovered
logos from attacked images.
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Table 5.4: PSNR of extracted watermark logo from watermarked test images after attacks
5.3.5.3 COMPARATIVE STUDY RESULTS WITH OTHER SCHEMES: We
compared our scheme against JPEG compression with other similar and state-of-the-art
methodologies which are well known for their robustness against JPEG compressions.
The chosen schemes are as follows:
Scheme-A: Correlation based Schemes with 2 PN sequence (Section 2.1.3.2)
Scheme-B: Classical MBCE Scheme (Section 2.2.2.1)
Scheme-C: Scheme proposed in Section 4.4 is also based on Middle Band Coefficient
Exchange (MBCE) scheme and ICAR in nature. So, we decided to compare the
performance of our scheme with this scheme also. This scheme swaps 4 pairs of
coefficients in FM region in correlation with low band coefficients. We are naming this
scheme as Scheme-C.
Then, we re-implemented the chosen schemes for the colored images and hid the
watermark data in BLUE channel.
Scheme-D: We are naming our proposed scheme as Scheme-D.
It is observed that all the above schemes are robust against JPEG compression attack but
if we compress the watermark images by very low quality factors (less then Q = 20), our
proposed scheme outperforms the other schemes. We compressed the watermarked test
PSNR (DB)
His
togr
am
Equa
lizat
ion
Zoom
Brig
htne
ss-
Con
trast
A
djus
tmen
t
Hue
-Sa
tura
tion
Gau
ssia
n N
oise
Gau
ssia
n B
lur
Lena 34.67 34.67 34.67 34.67 34.67 34.67
Mandrill 28.06 28.04 28.04 28.04 28.04 28.04
Pepper 32.25 32.07 30.78 32.48 31.78 31.10
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images by keeping JPEG quality factor Q = 15, 10, and 5. No scheme, other than the
proposed one, was able to extract the detectible watermark logos.
Figure 5.4: Recovered logos from attacked images
Table 5.5 summarizes the PSNR values of extracted logos from highly compressed
watermark test images using various schemes. Figure 5.5 shows the recovered watermark
logos from highly compressed watermarked images using our proposed scheme. It may
be observed that recovered logos are quite detectible and proposed scheme is more
efficient than the other chosen schemes.
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Table 5.5: PSNR values of extracted logos from highly compressed watermarked test images using various schemes
PSNR (DB)
Schemes
JPEG
Quality
Factors Lena Mandrill Pepper
Q = 15 8.723 7.89 8.12
Q = 10 7.67 7.12 7.988 Scheme-A
Q = 05 4.5 4.324 4.657
Q = 15 4.222 4.587 3.987
Q = 10 3.45 3.87 3.95 Scheme-B
Q = 05 2.32 2.2 1.97
Q = 15 4.323 4.565 4.33
Q = 10 4.11 4.249 4.12 Scheme-C
Q = 05 2.234 2.229 2.1
Q = 15 16.305 10.845 13.335
Q = 10 15.585 10.62 12.885 Scheme-D
Q = 05 14.13 10.29 11.4
Test Images /
JPEG Q Factor
Lena Mandrill Pepper
Q = 15
Q = 10
Q = 05
Figure 5.5: Extracted logos using proposed scheme from highly compressed watermarked test images
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Results indicate that proposed scheme recovers the watermark even from an attacked
image which is compressed up to Q = 5 quality factor of JPEG (i.e. after 95 - 99% size
reduction).
This proves that the proposed scheme is not only an ICAR scheme but also very robust to
JPEG compression. In addition to this, the proposed scheme is resisting common image
manipulations like cropping, scaling, flipping, histogram equalization, brightness-
contrast adjustment, Hue-saturation alteration, Gaussian noise and Gaussian blur.
5.4 CONCLUSION
In this chapter, we have discussed the watermarking of the colored images. Since a
colored image has R, G and B color channel, firstly we presented a study to find the
suitability of a color channel to carry the watermark data with respect to the robustness
against an attack. It was found that if an image has to undergo JPEG compression attack,
then the watermark data should be hidden in GREEN color channel to ensure the best
recovery of the watermark logo. Then, we presented an ICAR watermarking scheme
based on the “average” of the FM coefficients. Results indicted that the proposed scheme
is very robust against JPEG compression and common image manipulations and better
then other similar state-of-the-art schemes.
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CHAPTER-6
WATERMARKING OF JPEG IMAGES
6.1 INTRODUCTION
In the Chapter 4, we have discussed that we can improve the robustness of DCT and
DWT based watermarking schemes against some well known attacks by preprocessing
the images. Since, “Fingerprinting” is the most crucial demand of today, we developed an
ICAR scheme for the watermarking of gray level images also. We further expanded our
scope for the colored images watermarking in Chapter 5 and developed an ICAR scheme
for watermarking of 24-bit colored BMP images. Since, most of the images present on
World Wide Web are in JPEG format, which is a highly compressed image format and
store the images in the transformed domain, i.e. store the frequencies not the pixels
values, we decided to develop an ICAR watermarking scheme for JPEG images. We also
explored a relationship between the robustness and some of the image characteristics.
6.2 DEVELOPMG AN ICAR WATERMARKING ALGORITHM FOR JPEG IMAGES
Most of the images present on WWW are in the Joint Photographic Experts Group
(JPEG) format where as relatively less work is found for watermarking the JPEG images.
Therefore, we decided to extend our earlier proposed ICAR schemes for the
watermarking of JPEG images also. In our earlier proposed ICAR schemes, we inserted
the ICAR nature in by introducing redundancy in the coefficients swapping of FM region.
We also made the swapping criteria dependent on some very robust data elements (in the
scheme presented in Section 4.4, it was the relative value of low frequency coefficient
and in the scheme presented in 5.3, it was the average value of all middle band
coefficients) so that decoding algorithm may perform a good recovery of the watermark
data. But as it may be observed that we deployed the coefficients of FM region which
were generated by taking the 8 x 8 DCT of pixels values. So, to continue the same
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approach for the JPEG images, we needed to use coefficients belonging to FM region.
More pricelessly, JPEG image format does not store the pixel’s actual value but it stores
the image in frequency domain. So, we need to convert the JPEG image into spatial
domain and then take 8x8 block DCT on its color channels to get the FM region.
To inject the ICAR nature, we need to introduce redundancy in coefficient swapping.
Since JPEG is a very high compressed format, we know that as soon as we convert this
spatial domain image into JPEG format, lots of its coefficients will be changed. This
would create problem in recovering the watermark data by only considering the relative
strengths of coefficients of FM region. We must, therefore, provide extra robustness by
involving some coefficients whose value does not change much during the conversion of
spatial domain to frequency domain and vise versa. To resolve this issue, we decided to
take the advantage of JPEG compression-decompression scheme itself. In an 8x8 DCT
block, large value of the top-left corner is called the DC coefficient. The remaining 63
coefficients are called the AC coefficients. This DC coefficient is the major dominating
value while decompressing. This DC value alone can regenerate the best approximated
image by taking the IDCT. If this value is altered, then image is largely affected. So we
decided to take the contribution of this DC coefficient apart from coefficients from FM
region to interpret the watermark data to make our scheme robust. We have seen that in
our earlier scheme, we developed a swapping criteria based on the average of all 22
coefficients of FM region by claiming that it was difficult for any attacker or for any
image manipulation to alter this value significantly if the image has to remain
perceptually similar. Therefore, for our newly proposed watermarking scheme for JPEG
images, we interpreted the watermark data in FM region based on the average of 22
coefficients from FM region and the DC coefficient. More details of the watermark
embedding algorithms are described in Section 6.2.3. To ensure ICAR property, liker our
earlier proposed schemes, we watermarked each copy of a single JPEG image with a
different policy.
The proposed watermarking scheme can be defined as a 7-tuple (X, W, P, T, G, E, D)
where:
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1. X denotes the set of instances Xi, of a particular JPEG image, (If N copies of
an image are to be watermarked, then 0 ≤ i ≤ N);
2. W denotes the monochrome watermark logo;
3. P denotes the set of policies Pi, 0 ≤ i ≤ N;
4. “T” is the “watermark strength parameter”;
5. G denotes the policy generator algorithm G: Xi Pi, where
Each Xi will have a unique Pi, i.e. a different policy to hide the watermark
data;
6. E denotes the watermark embedding algorithm, E: Xi x W x Pi Xi’;
7. D denotes the watermark detection algorithm, D: Xi’ x Pi W’, where W’
represents the extracted watermark.
The parameter “T” is analogous to “K” of classical MBCE scheme. In classical MBCE
scheme, relative strength of two coefficients value of FM region decides the decoding of
“1” or “0”. If the relative strength of two values has to decide the decoding of “0” or “1”,
then larger value should remain larger even after image manipulations. So, we adjust
these values in such a way that the difference between the two values becomes larger
than a certain threshold value. We name this threshold value as “Watermark Strength
Parameter” because this value decides the robustness of watermark data. Certainly, it has
an impact on the image perceptibly. So, we need to decide this threshold value in such a
way that our image does not loose its quality much. The value of “T” may differ for each
image.
Out of these 7 tuples, last 3 tuples are algorithms, which are discussed below:
6.2.1 G, THE POLICY GENERATOR ALGORITHM
Similar to our earlier proposed ICAR watermarking scheme for the gray image
watermarking and colored image watermarking, we need to watermark each copy Xi of
an JPEG image X differently. Therefore, we need a different watermarking policy for
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each copy of the image to be watermarked. Here “Policy” means that, for every copy of
the image, there will be unique combination of 4 middle band coefficients. First we had
to convert the source JPEG image into its equivalent true colored 24-bit BMP image.
Then, to generate a policy, we simply take 8 x 8 DCT of a chosen color channel of the
input image Xi and randomly select 4 coefficients out of 22 middle band coefficient of
FM region from any of the red, green or blue color channel. So, numbers of policies that
can be generated are 22C 4 = 7315 which means that 7315 copies of a single image can be
watermarked such that no two watermarked images have same policy. This step ensures
that attacker can not conclude the location of watermark data by colluding many
watermarked copies of an image. This also depicts that our proposed scheme is an ICAR
scheme. Policy generator algorithm also returns the color channel to be used to carry the
watermark.
6.2.1.1 COLOR CHANNEL SELECTION: Bossen et al. [9] have stated that the
watermarks should be embedded mainly in the BLUE color channel of an image because
human eye is least sensitive to change in BLUE channel. However, the suitability of color
channel to hide the watermark data depends on the image itself. The color channel which
should be used can be found on the basis of the amount of the color present in the image
or on the basis of histogram of each color channel (i.e. color with spreader histogram
should be given priority). We also know that for few images, BLUE channel may not
give the optimum results. We, therefore propose that the color channel with the lowest
“Standard Deviation (SD)” should be selected. More details of this finding and result
related to this issue are given in the Section 6.2.4.1.
6.2.2 E, THE WATERMARK EMBEDDING ALGORITHM
In this algorithm, each 8x8 DCT block of an image is used to hide a single bit of
watermark logo. Our embedding algorithm is based on averaging the coefficients of FM
region and the DC coefficient. As we know that attacker cannot alter this “average (Av)”
of coefficients of FM region and the DC coefficient badly as it will heavily impact the
quality of image, we are hiding “1” or “0” by using the relative values of four coefficients
with this “Av”.
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This algorithm is given as follows:
1. Repeat steps 2 to13 for i = 1…..n; // where ‘n’ is the number of copies of a single image to be watermarked //
2. INPUT (Xi);
3. Convert the Xi into its equivalent spatial domain 24-bit colored image;
4. Take 8 x 8 block DCT of Xi;
5. INPUT (W);
6. Convert W into a string S = (Sj | Sj = {0,1}, for j = 1…..length of the watermark);
7. Let L = STRING_LENGTH (S); // L is the length of watermark data. If L = 1000, then first 1000 DCT block of Xi are used //
8. Pi = CALL (G); // Each generated Pi shall be stored in an author’s database for the detection purpose in future.
Let the Pi for chosen Xi be, Pi = {(5,1), (4,2), (6,3) and (5,4)} in the chosen color channel //
9. Calculate the average “Av” of remaining 18 middle band coefficients and DC
coefficient.
Av = (DCT (0, 0) + Sum (22 Middle band coefficients) - Sum (4 chosen
coefficients chosen by Pi)) / 19.
10. Repeat steps 11 to13 for r = 1…..L;
11. Read Sr;
// Now like classical MBCE scheme, relative strength of average “Av” and chosen 4 coefficients
in step 7 will interpret “0” or “1” of watermark data. To hide “0” for all 4 chosen coefficients
in step 7, we assigned the value of coefficients which is ‘T’ less than the average “Av”. To hide
“1”, for all 4 chosen coefficients in step 7, we assigned the value of coefficients which is ‘T’
greater than the average “Av” //
If (Sr = 0)
DCT (5, 1) = Av - T;
DCT (4, 2) = Av - T;
DCT (5, 4) = Av - T;
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DCT (6, 3) = Av - T;
Else
DCT (5, 1) = Av + T;
DCT (4, 2) = Av + T;
DCT (5, 4) = Av + T;
DCT (6, 3) = Av + T;
End;
12. Take IDCT to reconstruct Xi;
13. Convert Xi back to its JPEG format;
14. End.
6.2.3 D, THE WATERMARK DETECTION ALGORITHM
Watermark extraction is the reverse procedure of watermark embedding. To extract the
watermark from the watermarked JPEG image, first we convert it into its equivalent 24
bit colored images and then calculate the average “Av” in a same way, as in embedding
algorithm. Owner has a record of all policies used to watermark the images. Based on
“policies”; owner of the image can recover watermark using following rule:
1) If at least 1 out of 4 chosen coefficients are less then Av, Interpret “0”; and
2) If at least 1 out of 4 chosen coefficients are greater then Av, interpret “1”.
The detection algorithm steps are as follows:
1. INPUT (Xi’); // Xi’ is the attacked copy of a watermarked image//
2. Convert Xi into its equivalent 24 bit colored image;
3. Take 8x8 block DCT of Xi’ and calculate Av;
4. For all Pi stored in author’s database, repeat the steps 5; // If initially 10 copies were watermarked, then out of 10 policies, for 1 policy, watermark will be
recovered correctly. To explain further steps, we are assuming that now algorithm is in a loop
where Pi is {(5, 1) (4, 2) (5, 4) and (6, 3)}, which was used to watermarked this particular Xi’ //
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5. Repeat the steps 5 for j = 1….L; // L is the length of watermark data. A single bit will be recovered form one 8x8 DCT block.//
Take jth DCT block to form jth bit of watermark as follows:
If (DCT (5, 1) < = Av)
T1 = 1;
Else T1 = 0;
If (DCT (4, 2) < = Av)
T2 = 1;
Else T2 = 0;
If (DCT (5, 4) < = Av)
T3 = 1;
Else T3 = 0;
If (DCT (6, 3) < = Av)
T4 = 1;
Else T4 = 0;
If ( T1 + T2 + T3 + T4 > = 1 )
Decode “0”
If (DCT (5, 1) > Av)
P1 = 1;
Else P1 = 0;
If (DCT (4, 2) > Av)
P2 = 1;
Else P2 = 0;
If (DCT (5, 4) > Av)
P3 = 1;
Else P3 = 0;
If (DCT (6, 3) > Av)
P4 = 1;
Else P4 = 0;
If ( P1 + P2 + P3 + P4 > = 1)
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Decode “1”;
End;
6. Store W’, the recovered watermark;
7. End.
It may be observed from both the algorithms that even if attacker alters the values of the
coefficient of FM region, if “Av” is not changed much, then we can recover the
watermark data correctly and attacker cannot aim to attack the image in such a manner
which modifies the “Av”.
6.2.4 PERFORMANCE OF THE PROPOSED SCHEME
Our proposed scheme does not need any testing to check whether or not it is robust
against the collusion attack as it is designed in such a way that the attacker can not
analyze the pattern by colluding many watermarked copies. We needed to check the
performance of the proposed scheme against the JPEG compression and other common
image manipulations and known attacks. We have tested our scheme on four JPEG test
images of Lena, Mandrill, Pepper and Goldhill shown in Figure 3.12 and watermark logo
is shown in Figure 3.13. We measured the image quality in terms of Peak Signal to Noise
Ratio (PSNR) and Correlation Coefficient (CC).
Firstly, we choose an appropriate value of “T” which affects least the image quality as
well as optimizes the recovery of the watermark data. Based on our earlier experiences
discussed in Section 5.3.5, we embedded the watermark logo in test images by keeping
T = 150 (in blue color channel) and then recovered watermark logos. Our experiments
suggested that in Lena, Mandrill and Pepper test images, there was, almost no loss in the
perceptual quality of the images (as shown in Figure 6.1) and recovered watermark logos
were of very fine quality. Figure 6.2 shows the watermark logos obtained from Lena,
Mandrill, Pepper and Goldhill. It was observed that for Goldhill test image, recovery was
not good. Therefore, we continued to experiment the same process for the Goldhill test
image at various values of T and we found that at T = 100, Goldhill test image was giving
the best recovered logo without much loosing its perceptibility. Figure 6.3 shows the
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goldhill test image after the watermark logo was embedded and the recovered logo.
Therefore, considering the “imperceptibility versus Robustness” trade-off, we fixed up
the value of T = 150 for the further tests for Lena, Mandrill, and Pepper JPEG test
images, and T = 100 for the Goldhill test image.
Figure 6.1: Watermarked test images generated by keeping T = 150
Figure 6.2: Extracted watermark logos from watermarked Lena, Mandrill, Pepper and Goldhill test
images respectively at T = 150
Figure 6.3: Goldhill test image after hiding the watermark logo and the recovered logo at T = 100
6.2.4.1 COLOR CHANNEL SELECTION AND PERFORMANCE AGAINST
JPEG COMPRESSION: Standard deviation (SD) depicts the spread of the frequency
values in a range. If the histogram of a chosen color channel of a particular image has less
spread, the image has less number of frequencies of the chosen color channel. Since, it is
the color channel i.e. the particular color frequencies that actually carry the watermark
data, we conclude that SD must play an important role. To explore the relationship
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between the selection of a color channel to carry the watermark data and the efficiency of
recovery, we decided to experiment on SD of all three color channels. Table 6.1 shows
the standard deviation of all three color channels for test images.
Table 6.1: SD values of color channels for test images
Lena Mandrill Pepper Goldhill
R channel 49.05 55.5 45.17 56.6
G channel 52.88 47.78 75.05 54
B channel 34.06 61.7 44.29 61
First, we hid the watermark data in the BLUE channel of all four test images. Then, we
compressed watermarked images using JPEG technique at various quality factors and
then recovered the watermark logos. We calculated the PSNR and CC values of extracted
logo. Table 6.2 summarizes the results. It was found that extracted watermark from
Mandrill and Goldhill test images were having poor values of PSNR and CC. Therefore,
for these two images, we repeated the above process by using “GREEN’ Channel. The
qualities of the extracted watermark logos from these two images were improved.
Therefore, we have related the performance of our scheme with color channel selection.
As, it may be observed from the Table 6.1 that for Lena’s and Pepper’s test images,
BLUE channel have lesser SD, whereas for Mandrill’s and Goldhill’s images, GREEN
channel has lesser SD. So it was concluded that lesser the SD better is the recovery of the
watermark data. This fixed up the BLUE channel for Lena’s and Pepper’s watermarking
and GREEN channel for rest two images. It is clear from Table 6.2 and Table 6.3 that
after using GREEN channel for Mandrill’s and Goldhill’s images, performance was
increased. It may be further observed from Table 6.3 that our proposed scheme is quite
robust against JPEG compression.
6.2.4.2 PERFORMANCE AGAINST IMAGE MANIPULATIONS: We performed
the following attacks on the watermarked test images:
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Attack-1: Equalize the Histogram;
Attack-2: Add 10 % Uniform noise;
Attack-3: Adjust the brightness to + 40 and contrast to + 25;
Attack-4: Adjust the hue and saturation to + 10 each;
Attack-5: Flip Horizontal; and
Attack-6: Apply uniform scaling (Zoom).
Our proposed scheme sustained all the attacks and qualities of extracted watermark logos
were very fine. Table 6.4 summarizes the CC of extracted logos from all test images.
Figure 6.4 shows the recovered logos from attacked images.
Table 6.2: PSNR and CC of extracted logo by using BLUE channel for all images
JPEG
Quality
Factor Lena Mandrill Pepper Goldhill
PSNR 20.898 10.53 24.876 12.53
Q = 60 CC 84.78 51.8 90.55 54.8
PSNR 21.672 9.756 25.412 12.11
Q = 40 CC 86.25 46.11 91.16 48.54
PSNR 19.597 9.27 23.508 9.88
Q = 20 CC 82.59 41 88.95 45.76
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Table 6.3: PSNR and CC of extracted logo by using BLUE and GREEN channels for images
JPEG
Quality
Factor
Lena
(BLUE),
T = 150
Mandrill
(GREEN)
T = 150
Pepper
(BLUE)
T = 150
Goldhill
(GREEN)
T = 100
PSNR 20.898 21.06 24.876 22.31
Q = 60 CC 84.78 85.12 90.55 91.45
PSNR 21.672 20.682 25.412 23.32
Q = 40 CC 86.25 84.98 91.16 92.56
PSNR 19.597 20.682 23.508 21.43
Q = 20 CC 82.59 84.97 88.95 91.45
6.2.4.3 COMPARATIVE STUDY WITH SIMILAR, STATE-OF-THE-ART
SCHEMES: We compared the performance of the proposed scheme against JPEG
compression with other similar schemes which are DCT based and well-known for their
robustness against JPEG compression.
Table 6.4: CC of the extracted logos
Test Images /Attacks
Lena
(BLUE),
T = 150
Mandrill
(GREEN),
T = 150
Pepper
(BLUE),
T = 150
Goldhill
(GREEN),
T = 100
Histogram Equalization 83.82 82.15 84.04 81.30
Uniform Noise (10%) 57.97 80.64 58.37 79.75 Brightness (+ 40) & Contrast
(+ 25) 81.05 77.13 80.69 76.25 Hue and saturation adjust (10
each) 86.09 85.62 86.35 85.65
Horizontal Flip 97.01 96.98 96.56 96.36
Uniform scaling 92.31 91.67 92.41 92.27
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Figure 6.4: Extracted logos from attacked watermarked images
The schemes chosen were:
Scheme-A: Correlation based Scheme (Section 2.1.3.1)
Scheme-B: The Classical Middle Band coefficient exchange scheme (Section 2.2.2.1)
Scheme-C: Collusion attack resistant watermarking scheme (Section 4.4): Scheme
proposed in Section 4.4 is also based on MBCE scheme and ICAR in nature. This scheme
swaps 4 pairs of coefficients in FM region in correlation with low band coefficients. We
are naming this scheme as Scheme-C.
Scheme-D: We named our proposed scheme as Scheme-D.
We re-implemented the first three chosen schemes ideas for JPEG colored images. In
their work “A Novel DCT-based Approach for Secure Color Image Watermarking” [7]
author have compared their proposed scheme against JPEG compression with Tsai [102],
cox [19], Fridrich [28] and Koch [48] approaches but they have given the results only up
to JPEG Quality factor Q = 20. Therefore, we compared our proposed scheme for very
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less JPEG quality factors such as Q = 5 and Q = 10. Most of the schemes started loosing
their efficiency at these quality factors.
We conclude that all the above schemes were very robust against JPEG compression
attack but if we compressed the watermark images at very low quality factors (less than
Q = 20), our proposed scheme outperformed the other schemes. No scheme, other than
the proposed one, was able to extract the detectible watermark logo at Q = 10 and 5.
Figure 6.5 shows the graph of CC values of recovered logos obtained from JPEG
compressed (at Q = 10) images which were watermarked using various schemes.
Figure 6.6 shows the graph of CC values obtained from JPEG compressed (at Q = 5)
images.
Therefore, the proposed scheme is not only an ICAR scheme but also enhances the
performance. Results indicate that the proposed scheme recovers the watermark even
from highly attacked images which are compressed up to Q = 5 quality factor of JPEG
(i.e. after 95-99% size reduction). In addition to this, the proposed scheme is resisting
common image manipulations like cropping, scaling, flipping, histogram equalization,
brightness- contrast adjustment, hue-saturation alteration and Gaussian noise.
0102030405060708090
100
Lena(Blue)
Mandrill ( Green)
peper(Blue)
Goldhill(Green)
Scheme-A Scheme-BScheme-C Scheme-D
Figure 6.5: Comparison of correlation coefficients at Q = 10
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Figure 6.6: Comparison of correlation coefficients at Q = 5
6.3 A DWT BASED WATERMARKING SCHEME FOR JPEG IMAGES
During the development of the above schemes, popularity of JPEG2000 image
compression/encoding increased. JPEG2000 is a wavelet-based image compression
standard. This standard has also been created by the Joint Photographic Experts Group
committee in the year 2000 with the intention of superseeding their original DCT based
JPEG standard (created in the year 1991). The standardized filename extension for
JPEG2000 image is .jp2. JPEG 2000 has a much more significant advantage over certain
modes of JPEG in that the artifacts are less visible and there is almost no blocking. The
compression gains over JPEG are attributed to the use of DWT and a more sophisticated
entropy encoding scheme.
Since .jp2 format is new upcoming image format and very less watermarking efforts have
been presented against this format conversion in the literature, we need to focus this
attack because BMP and JPEG images may have to undergo .jp2 image format
conversion/compression. To ensure that our watermarked images do not lose their
robustness against JPEG2000 format conversion attack, we further develop a
watermarking scheme which can sustain JPEG2000 format conversion attack also.
0102030405060708090
Lena(Blue)
Mandrill (Green)
peper(Blue)
Goldhill(Green)
Scheme-A Scheme-BScheme-C Scheme-D
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6.3.1 EXPLORATION OF DWT DOMAIN
Till now, all of our proposed watermarking schemes are DCT based, and therefore, very
robust against JPEG compression attack because JPEG encodes the images using DCT.
Both, DCT and DWT encode (or compress) the image very differently. Since JPEG2000
encodes the image using DWT, a DCT based scheme may not be fruitful if we are
targeting .jp2 conversion attack resistant nature in our watermarking scheme. Our earlier
results of “preprocessing” (Sections 4.2) also supported this fact. So, we decided to
explore DWT domain for the watermarking of JPEG images.
6.3.1.1 ISSUES IN USING DWT: Because of their inherent multi-resolution nature,
wavelet-coding schemes are especially suitable for applications where scalability is
important. The use of DWT is gaining popularity in signal processing, image
compression and watermarking. DWT gives extremely good results in the case of lossless
compression. But DWT has a serious issue when it comes to comparison with DCT for
the watermarking purposes. We cannot assume lossless manipulation in images; both in
watermark embedding and while the image is being attacked. In watermarking, one has to
ensure that the watermark data is recoverable even from highly
destroyed/manipulated/compressed/lossy cover image. Now, while using DCT domain, in
most of the cases, we take 8 x 8 DCT and thus have hundreds of DCT blocks. In each
DCT block, there are FL, FM and FH regions, as shown in Figure 2.3. We cannot use FH
because any manipulation operation will attack first on FH. FL has the major dominating
coefficient to recreate the image. If we use FL to hide the watermark data, cover image
perceptibility will be affected seriously. Therefore, we use FM region, or since, there are
so many FL regions, we can work out to devise a watermarking scheme that takes FL
region also into consideration without changing FL coefficient values.
On the other hand, DWT takes the complete image into consideration as shown in
Figure 6.7 and breaks it into four parts, namely LL, HL, LH, and HH region. This policy
may have several advantages but for watermarking, it has a very serious issue. Like DCT
blocks, we should not use HH region. LL region coefficients can also not be altered much
because these will heavily affect the image perceptibility (LL coefficients will alone
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generate a very good approximated image and we cannot alter these coefficients much).
HL and LH coefficients may be altered seriously by any image manipulation operation.
Unlike DCT based transformation (where there are so many FM regions to hide the
watermark data), there is only one LL region in DWT. Therefore, we have very less space
to hide the watermark data. Either we disturb heavily DWT coefficients and thus affect
the image perceptibility while hiding watermark data or to preserve to image quality, hide
watermark data in those regions which are less susceptible to get modified by image
manipulation operations and thus affecting the robustness of the watermarking scheme.
We thus conclude that if we use DWT for watermarking purpose, “Imperceptibility vs.
Robustness” balance is the new challenge for us. More precisely, the classical CDMA-
DWT based scheme as given in Section 2.2.3.1, a highly referred scheme which is very
robust against JPEG compression, affects the image quality up to a great extent. On the
other hand, if sub-band based technique [36] does not affect the image perceptibility after
hiding the watermark data, we may recover the watermark data from JPEG compressed
image only up to compression ratio 10-15 (Q = 70 approx). So, both the above well-
known schemes do not have a good balance in “Imperceptibility vs. Robustness” trade-
off.
Figure 6.7: 2-D Haar DWT
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Therefore, in this section, our target is to develop a watermarking scheme which is:
1) ICAR in nature (because it ensures the maximum coverage of financial
implications.)
2) JPEG2000 attack resilient (because it is upcoming DWT based image format).
3) Being a DWT based scheme, achieve a good balance in “Imperceptibility vs.
Robustness” trade-off, as most of the DWT based watermarking scheme do not
satisfy much of this quality.
We decided to explore Haar DWT for watermarking purposes because CDMA-DWT
[42][52] and Sub-band based scheme [36] used Haar DWT and in both these schemes,
use of Haar DWT has shown its robustness against “JPEG compression” as well as
“image imperceptibility” separately.
6.3.2 BACKGROUND OF THE PROPOSED SCHEME
We used a monochrome logo as a watermark data which we first converted into a string
of ‘0’s and ‘1’s. Now, we needed to hide ‘0’ and ‘1’, in our JPEG image, which we
converted into its equivalent RGB image. As we have said above that a single DWT
block of the image does not give us enough space to hide the data, we planned to take 8 x
8 DWT on a specified color channel of JPEG so that we have a large number of DWT
blocks and thus have enough opportunities to hide the watermark data. We used color
channel with lesser Standard Deviation (SD) (as discussed in Section 6.2). We inherited
the idea of classical MBCE scheme i.e. instead of actually embedding any data, we
interpret ‘0’ or ‘1’ by using the relative strength of two values. We claim that the
“average” value of all coefficients of a single LL region is less susceptible to
modification because LL coefficients are the major dominating coefficients and one
cannot change all coefficients much. Even if some of these have been altered after one
pass of coding and decoding (Taking DWT and then IDWT), the altered coefficients will
again try to get their original value (if we are not changing the perceptual quality of the
image). Therefore, “Average of all LL coefficients” may provide us a good robustness.
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Even if it is slightly modified, it is very less probable that relative values of “Averages”
of two consecutive LL blocks get modified. So we decided to hide “0” or “1” by using
the relative value of average of LL coefficients of two consecutive 8x8 DWT blocks.
6.3.3 DUAL WATERMARKING
Both DCT and DWT encode the image very differently. Since DWT based watermarking
scheme provides coverage against DWT based attack, our watermarking scheme may not
give good result against DCT transformation based attacks as in the case of JPEG
compression. Since we have a very robust DCT based scheme in hand (proposed in
Section 6.2), we decided to watermark the images using both schemes, one after another,
to ensure the maximum coverage against attacks.
So, first we watermark an image (I) using a DWT based approach to generate a
watermarked copy (I’) and the on I’, we again apply a DCT based scheme, presented in
Section 6.2, to generate a final watermarked copy I’’.
6.3.4 THE DWT BASED WATERMARKING
In our proposed dual watermarking, the DWT based watermarking scheme for JPEG
images is defined as a 7-tuple (X, W, P, T, G, E, D) where:
1. X denotes the set of instances Xi, of a particular JPEG image, (If N copies of
an image are to be watermarked, then 0 ≤ i ≤ N);
2. W denotes the monochrome watermark logo;
3. P denotes the set of policies Pi, 0 ≤ i ≤ N;
4. “T” is the “watermark strength parameter”.
5. G denotes the policy generator algorithm G: Xi Pi, where each Xi will have
a unique Pi, i.e. a different policy to hide the watermark data. This ensures the
ICAR nature;
6. E denotes the watermark embedding algorithm, E: Xi x W x Pi Xi’; 7. D denotes the watermark detection algorithm, D: Xi’ x Pi W’, Where W’
represents the extracted watermark.
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6.3.4.1 P, THE POLICY: P is a set of policies Pi, where each Pi belongs to a unique Xi,
the instance of an image. A Pi is generated by G and is of the form (Starting block (r,s),
offset, color channel). For example, for Lena test image which we used in our
experiments, Pi is (starting block (0,0), offset (1), Blue).
6.3.4.2 G, THE POLICY GENERATOR ALGORITHM: Similar to our earlier
proposed ICAR watermarking scheme for the gray image watermarking and colored
image watermarking, we need to watermark each copy Xi of a JPEG image X differently
to ensure the ICAR nature. Policy generator algorithm is called by E, after taking 8 x 8
DWT of the image. Since average of two consecutive LL blocks have to interpret “0” or
“1”, G ensures that no two copies of the same original image use the same pattern. So, G
achieves this by providing E, the calling routine, a starting block (which is chosen
randomly) and an offset. E can start grouping of 2 consecutive blocks using this data. For
example, consider an image of size 80 x 80. There will be 100, 8 x 8 DWT blocks in each
color channel as shown in Figure 6.8 and 6.9. If G returns the starting block (0,0) and
offset 1 in a specific color channel, then the blocks to be chosen to hide the watermark
data are shown in Figure 6.8. If G returns the starting block (5,5) and offset 2, then blocks
to be chosen to hide the watermark data are shown in Figure 6.9.
We assume the circular queue of the DWT blocks. If our source image is of 512 x 512
size, then there are 4096, 8 x 8 DWT blocks. Using G, we can generate thousands of
policies which ensure that no two watermarked copies will share same way to hide the
watermark data. The overhead of this G is that the author / owner has to record all
policies in his / her database to use in the decoding phase. This depicts that our proposed
scheme is an ICAR scheme.
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Figure 6.8: An example of 2 consecutive DWT blocks
Figure 6.9: An example of 2 consecutive DWT blocks
These first 2 blocks will hide first bit
These 2 consecutives blocks will hide second bit
These 2 blocks will hide second bit
These 2 blocks, having offset 2, will hide first bit
(5, 5) block
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Policy generator algorithm also returns the color channel to be used to carry the
watermark data. As discussed in the Section 6.2.4.1, we used the color channel with
lesser Standard Deviation (SD) to hide the watermark data.
6.3.4.3 E, THE WATERMARK EMBEDDING ALGORITHM: To explain the
embedding algorithm, we assume that G returns the DWT (0, 0) block as starting block
with offset as 1. A simple watermark embedding approach is shown in Figure 6.10.
Embedding algorithm steps are as follows:
1. Repeat steps 2 to12 for i = 1…..n;
// where ‘n’ is the number of copies of a single image X to be watermarked//
2. INPUT (Xi); // Xi is the instance of X.
3. Convert the Xi into its equivalent spatial domain 24-bit colored image;
4. Take 8 x 8 block DWT of Xi;
5. INPUT (W);
6. Convert W into a string S = (Sj | Sj = {0,1}, for j = 1…..length of the watermark);
7. Let L = STRING_LENGTH (S); // L is the length of watermark data. If L = 1000, then first 2000 DWT block of Xi are used to hide
the watermark data //
8. Pi = CALL (G); // Each Pi shall be stored in an author’s database for the detection purpose in future. Let the Pi,
for chosen Xi, be Pi = {DWT (0, 0), Offset (1), BLUE} which is shown in Figure 6.8 //
9. Repeat steps 10 to 12 for r = 1…..L;
10. Read Sr; // Based on Pi, the average “AV1” and “AV2” of 2 chosen DWT blocks is calculated as follows: //
AV1 = (Sum of all LL coefficients of DWT (0, 0))/16;
AV2 = (Sum of all LL coefficients of DWT (0, 1))/16;
If (Sr = 0)
If (AV1 - AV2 > 0)
v = (AV1 - AV2) / 16;
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Subtract v from all coefficients of DWT (0, 0);
Add v in all coefficients of DWT (0, 1);
End;
// Now relative value of AV1 and AV2 reflects the watermark bit. To further increase the
robustness, we adjust the LL coefficients values such that difference of AV1 and AV2 become at
least ‘T’, the watermark strength parameter //
Subtract T / 2 from all LL coefficients of DWT (0, 0);
Add T / 2 in all LL coefficients of DWT (0, 1);
Else If (Sr = 1)
If (AV1 - AV2 < = 0)
v = (AV2 - AV1) / 16;
Subtract v from all coefficients of DWT (0, 1);
Add v in all coefficients of DWT (0, 0);
End;
// Now relative value of AV1 and AV2 reflects the watermark bit. To further increase the
robustness, we adjust the LL coefficients values such that difference of AV1and AV2 become at
least ‘T’, the watermark strength parameter //
Subtract T / 2 from all L coefficients of DWT (0, 1);
Add T / 2 in all LL coefficients of DWT (0, 0);
End;
11. Take IDWT to reconstruct Xi;
12. Convert Xi back to its JPEG format;
13. End.
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6.3.4.4 D, THE WATERMARK DETECTION ALGORITHM: Watermark
extraction is the reverse procedure of watermark embedding. To extract the watermark
from the watermarked JPEG image, first we converted it into its equivalent 24 bit colored
images, took 8 x 8 DWT and then calculated the average “AV” of consecutive blocks
based on policies stored in author’s database.
The detection algorithm steps are as follows:
LL LL
Divide image in 8x8 block
2 Consecutive 8x8 blocks will hide a single bit “0” or “1”
Take average of these 16 coefficients (AV1)
Take average of these 16 coefficients (AV2)
Adjust AV1 and AV2 (By changing LL coefficients little bit) such that their relative values reflect the watermark bits “0” or ‘1”. If AV1 > AV2 => “1” else “0”.
So, 1000 watermark bits will be hidden using 2000 consecutive 8 x 8 DWT blocks. Then IDWT will be taken. After then, image will be dual watermarked using DCT based watermarking scheme presented in Section 6.2.
Generated using G
Now consider next 2 consecutive blocks to hide next bit and so on
Figure 6.10: Watermark embedding approach
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1. INPUT (Xi’);
// Xi’ is the attacked copy of a watermarked image //
2. Convert Xi’ into its equivalent 24 bit colored image;
3. Take 8 x 8 block DWT of Xi’ for the specific color channel;
// Based on Pi, author knows which color channel was used to hide the watermark data for a
specific image //
4. Repeat step 5 to step 7 for each Pi;
5. Based on each Pi, group the DWT blocks in pairs;
6. For i = 1 to 2 * (L-1) repeat step 7;
// L is the length of W //
7. For each pair (AV1, AV2) of DWT blocks;
If (AV 1 > AV2)
Decode ‘1’;
Else Decode “0”;
8. Reconstruct W’, the extracted watermark;
9. End.
6.3.5 THE DCT BASED WATERMARKING
After hiding the watermark logo using DWT based watermarking presented above, we
dual watermarked the images using DCT based watermarking presented in Section 6.2.
6.3.6 RESULTS
We applied the proposed dual watermarking scheme on three standard JPEG test images
of Lena, Mandrill and Pepper. In this section, we used a different watermark logo, which
is shown in Figure 6.11.
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Figure 6.11: The watermark logo
6.3.6.1 THE VALUE OF “T”: Our proposed DWT based scheme takes a watermark
strength parameter as an input. This T itself tries to balance the “Imperceptibility versus
Robustness” trade-off. To decide the optimal value of this parameter, we hid the
watermark data in test images at various values of T and then calculated the PSNR values
of the watermarked images. Some of those values (which will lead us to a final value) are
shown in Table 6.5. After this, the watermark data was recovered and the quality of the
watermark data was measured using Correlation Coefficient.
Table 6.5: PSNR of watermarked image and CC of extracted logo for various values of T
Lena Mandrill Pepper
T
(LL
Band)
PSNR of
color channel
CC of
recovered logo
PSNR of
color
channel
CC of
recovered logo
PSNR of
color
channel
CC of recovered
logo
500 30.23 64.36 31.2844 51.8 31.3257 56.49
600 28.65 71.88 29.7187 60.29 29.7521 63.19
700 27.32 74.7 28.3907 64.5 28.4188 65.76
Table 6.6: Revised Table 6.5
T = 500 T = 600 T = 700
PSNR 75.575 71.625 68.3
Lena CC 64.36 71.88 74.7
PSNR 78.211 74.29675 70.97675
Mandrill CC 51.8 60.29 64.5
PSNR 78.31425 74.38025 71.047
Pepper CC 56.49 63.19 65.76
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0
10
20
30
40
50
60
70
80
90
PSNR CC PSNR CC PSNR CC
Lena Mandrill Pepper
T=500T=600T=700
Figure 6.12: Graph of the values shown in Table 6.6
Table 6.5 represents the above results. It is obvious that for the higher values of “T”, the
PSNR values of the watermarked images decrease but at the same time, the CC of the
extracted logos increase. To decide the value of T, we first brought the values of PSNR
data in the range of CC data, by multiplying by 2.5 and then reproduced the Table 6.6.
Figure 6.12 shows the graph of the values shown in Table 6.6. It may be observed that
series for T = 600 is always lying between the series of T = 500 and T = 700. It means
that value T = 600 is the best value, under the “Imperceptibility versus robustness trade
off”. Similarly for other values of T, if PSNR value is good, CC value is poor and vice-
versa.
We conducted further tests by using T = 600 for all test images. It may be noted that our
target was to embed the JPEG2000 attack resistant nature using DWT based embedding
without loosing the robustness against those attacks which our DCT based scheme could
sustain. Therefore, first we hid the watermark logo using DWT based scheme, and then
checked its robustness against JPEG2000 attack. As presented in Table 6.6, the quality of
the watermarked image did not decrease considerably. We converted the watermarked
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JPEG images (without applying DCT based scheme) to JPEG2000 format. Then, we
recovered the watermark logos from these watermarked images (which are converted to
JPEG2000 format). Table 6.7 represents the CC coefficients of extracted logos.
Figure 6.13 shows the extracted logos from JPEG2000 converted watermarked Lena,
mandrill and Pepper’s test images.
Table 6.7: CC of extracted logos from JPEG2000 attacked images
Test Image CC
Lena 67.71
Mandrill 55.45
Pepper 58.94
Figure 6.13: Extracted logos from Lena, Mandrill and Pepper’s test images
It may be observed from Table 6.7 and Figure 6.13 that our proposed DWT based
watermarking scheme is capable of sustaining JPEG2000 format conversion attack.
In order to implement the dual watermarking scheme, we further applied the DCT based
scheme on the watermarked images which were generated by applying DWT based
scheme. Now we had to check the effect on the image perceptibility as well as robustness
against JPEG2000 format conversion attack. Table 6.8 shows the decrement in the PSNR
values after the application of DCT based scheme. Though decrement is natural, it is not
perceptually visible in the PSNR values. It is a compromise with the image quality to
make the watermarked images very robust against more DCT based attacks, which we
will present later in this section.
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Table 6.8: Decrement in the PSNR values after the application of DCT based scheme
PSNR if only DWT
based scheme is applied
PSNR if both DWT &
DCT based scheme are
applied
Lena 25.69 25.23
Mandrill 25.83 24.97
Pepper 24.15 23.76
After applying dual watermarking scheme, we again conducted the JPEG2000 format
conversion attack on the watermarked images. Now we had a choice. We could recover
the watermark logos either by applying DWT based recovery or by applying DCT based
recovery. Table 6.9 shows the CC values of the extracted watermark logos recovered by
both recovery methods which clearly indicate that DCT based recovery gave better
results.
Figure 6.14 shows the extracted logos using DWT based method and Figure 6.15 shows
the extracted logos using DWT based method.
Table 6.9: CC values of the extracted watermark logos recovered by both recovery methods
CC if DWT based
recovery is applied
CC if DCT based
recovery is applied
Lena 64.7 91.56
Mandrill 52.14 44.1
Pepper 57.4 88.35
Figure 6.14: Extracted logos using DWT based method
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Figure 6.15: Extracted logos using DCT based method
6.3.6.2 PERFORMANCE AGAINST JPEG COMPRESSION: We have seen in the
previous section that our proposed DCT based scheme is very robust against JPEG
compression attack. We need to check the robustness of the dual watermarking scheme
presented in this section against JPEG compression. We compressed all test images after
applying dual watermark at very low JPEG quality factor Q = 20, 10 and 5 and recovered
the watermark logos using DCT based recovery. Our proposed dual watermarking
sustained this attack very strongly even at Q = 5. Table 6.10 shows the CC of the
extracted logos. Figure 6.16 shows the extracted logos.
Table 6.10: CC of extracted logo from highly compressed jpeg image using DCT based recovery
Q = 20 Q = 10 Q = 5
Lena 94.39 77.36 76.5
Mandrill 55.95 60.73 57.24
Pepper 92.63 80.35 76.37
Q = 20 Q = 10 Q = 5
Lena
Mandrill
Pepper
Figure 6.16: Extracted logos from highly compressed JPEG images
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6.3.6.3 PERFORMANCE AGAINST COMMON ATTACKS AND IMAGE
MANIPULATIONS: Since we know that transform domain based schemes are very
robust against those attacks which can reduce the size but not the perceptual quality, we
conducted some attacks on our dual watermarked images, which change the perceptual
quality of an image too. The attacks are as follows:
Attack 1: Adding uniform noise (10%),
Attack 2: Adding Gaussian noise (10 %),
Attack 3: Equalizing histogram,
Attack 4: Applying uniform scaling,
Attack 5: Adjusting brightness (+ 40) and contrast (+ 25),
Attack 6: Horizontal flipping, and
Attack 7: Adjustment of hue and saturation (+ 10 each).
Table 6.11 shows the CC of the extracted watermark logos and Figure 6.17 shows the
extracted watermark logos. It may be observed that the proposed dual watermarking
scheme sustained all the above mentioned attacks.
6.3.6.4 COMPARATIVE STUDY WITH DCT BASED SCHEMES: We compared
the performance of the proposed scheme against JPEG compression with other similar
schemes which are DCT based and well known for their robustness against JPEG
compression. We re-implemented these schemes for JPEG images. Schemes chosen
were:
Scheme-A: Correlation based scheme (Section 2.1.3.1)
Scheme-B: The classical Middle Band Coefficient Exchange scheme (Section 2.2.2.1)
Scheme-C: Collusion attack resistant watermarking scheme (Section 4.4): Scheme
proposed in Section 4.4 is also based on MBCE scheme and ICAR in nature. This scheme
swaps 4 pairs of coefficients in FM region in correlation with low band coefficients. We
are naming this scheme as Scheme-C.
Scheme-D: The scheme presented in Section 6.2.
Scheme E: The proposed dual scheme in this section.
128
Table 6.11: CC of the extracted watermark logos
Attacks Lena Mandrill Pepper
Adding uniform noise (10%) 80.6 52.39 84.83
Adding Gaussian noise (10 %) 56.8 47.42 74.56
Equalizing histogram 91.45 52.42 92.73
Applying uniform scaling 91.56 53.67 93.11
Adjusting brightness (+ 40) and contrast (+ 25) 87.3 53.95 89.12
Horizontal flipping 90.46 51.68 92.12
Adjustment of hue and saturation (+ 10 each) 93.75 56.78 92.87
Figure 6.17: Extracted watermark logos after applying common attacks
129
We watermarked the test images by using all chosen watermarking schemes and then
conducted very low JPEG compression (up to Q = 5, whereas most of the research papers
presented results only up to Q = 20). Then we calculated the CC of the extracted logos. It
may be observed from Table 6.12 and 6.13 that Scheme-E performs better than Scheme-
A, B and C. As compared to scheme-D, Scheme-E did not lower the performance but at
some point (Lena’s image at both Q = 5 and 10, and Pepper’s image at Q = 5) improves
the CC of the extracted logos.
6.3.6.5 COMPARATIVE STUDY WITH DWT BASED SCHEMES: As compared
to Classical CDMA-DWT based schemes presented in Section 2.2.3.1, our scheme
outperforms in the quality of the watermarked image (refer Table 6.8).
Table 6.12: Comparison of CC of Extracted logos from JPEG compressed (Q = 10) watermarked images
Lena Mandrill Pepper
Scheme-A 5.6 6.5 4.5
Scheme-B 4.5 6.7 6.5
Scheme-C 12.23 12.65 11.65
Scheme-D 71.12 74.53 84.28
Scheme-E 77.36 60.73 80.35
Table 6.13: Comparison of CC of Extracted logos from JPEG compressed (Q = 5) watermarked images
Lena Mandrill Pepper
Scheme-A 3.98 5.34 3.40
Scheme-B 3.47 5.02 4.98
Scheme-C 10.59 11.21 10.24
Scheme-D 72.32 72.33 75.04
Scheme-E 76.5 57.24 76.37
130
Sub-band filtering based watermarking scheme [36] is very good in preserving the
perceptual quality of the watermark images but when it come to the robustness against
JPEG compression, authors presented results only up to the compression ratio 10 to 15
whereas our proposed watermarking scheme can decode the watermark data up to the
quality factor Q = 5 (refer Table 6.13) i.e. the compression ratio 2 to 3.
It further proves that our proposed scheme has achieved a very good balance in
“imperceptibility versus robustness tradeoff while using DWT based watermarking
scheme.
6.4 CONCLUSION
In this chapter, we provided 2 watermarking schemes for watermarking the JPEG images.
The first scheme is DCT based and the other one is a dual watermarking scheme having a
DWT based watermarking as a component. Both schemes are very robust especially
against JPEG compression and other common image manipulation and attacks. Both
schemes also achieve a very good balance in “Image-imperceptibility vs. robustness”
trade-off and are ICAR in nature.
131
CHAPTER 7
RESULTS & CONCLUSIONS
7.1 SUMMARY
This research was taken up with an objective of developing watermarking algorithms for
images. We felt that it is essential to first ensure that all the developed watermarking
schemes are resistant to at least one attack having the most financial implications to
establish a high demand in the commercial market. Therefore, we chose collusion attack
resistant nature to be embedded in all developed watermarking schemes. Finding the
results reported in literatures, we started to develop the watermarking schemes by
choosing classical Middle Band Coefficients Exchange scheme as a base because this
scheme was very robust against JPEG compression attack. Chapter 4 discusses the
development of watermarking algorithm for Gray scale images and “preprocessing” of
the images to add robustness against JPEG compression and histogram equalization
attack. Chapter 5 presented a watermarking algorithm for colored BMP images and a
study to find the appropriate color channels to carry the watermark data to improve the
robustness of the watermarking scheme. In both chapters, the watermarking algorithms
are DCT based. After then, we presented a DCT based and a dual (DWT + DCT) based
watermarking schemes in Chapter 6 for JPEG images.
7.2 MAIN CONTRIBUTIONS AND HIGHLIGHTS OF THE
RESULTS The proposed watermarking schemes have the following characteristics:
1) These are ICAR in nature. This was achieved by introducing randomness and
redundancy in the coefficients exchange criterion in FM region of block DCT and
DWT.
132
2) These are very robust against JPEG compression (even up to JPEG quality factor
Q = 5, compression ratio up to 98 %) and outperform other existing state-of-the-
art watermarking schemes.
3) These are very robust against common image manipulations and known attacks.
4) We introduced the concept of “preprocessing” i.e. minimizing the attack impact
before watermarking of the image so that some known attack can not disturb the
watermarked images very heavily and thus perform better recovery of the
watermark data.
5) We established that if we want to increase the robustness against JPEG
compression, the watermark data should be hidden in the GREEN color channel
(not in BLUE color channel, as reported in most of the research papers even if
poor sensitivity of the eye to the BLUE color channel).
6) We have also correlated image characteristic and watermarking scheme
robustness against some attacks. It was found that for multicolored image, if
watermark data is hidden in the color channel having the lowest “Standard
Deviation”, then the performance against JPEG compression and other common
attacks increases.
7.3 FUTURE WORK
1) Considering the huge financial aspects of the watermarking application areas,
apart from ICAR, more characteristics against some attacks like forgery attack or
multiple watermarking can be embedded.
2) Further studies may be conducted to know the attack impacts on the images and
then watermarking schemes to be developed so that those impacts could be
minimized before the start of watermarking so that a better recovery of the
copyright data could be performed.
3) A watermarking scheme may have some relationship with the image on which it
is going to apply. Performance of the watermarking scheme or selection of the
watermarking scheme or at least few input parameters of the watermarking
scheme must be related to image characteristics.
133
4) More wavelet transforms should be examined for embedding of the watermark
data and robustness against JPEG2000 format conversion.
5) All presented watermarking schemes may be coupled with some special
“Geometrical attack resistant” watermarking schemes and thus developing
watermarking schemes to ensure maximum converge against malicious attacks.
134
135
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147
LIST OF AUTHOR’S PUBLICATIONS
[1] Saxena Vikas, Gupta J.P, “A Novel Watermarking Scheme for JPEG Images”, to appear in WSEAS Transaction on Signal Processing (June 2009 issue), ISSN: 1790-5022, Editor and reviewers comments received-Accepted.
[2] Saxena V. et al., “Performance Analysis of Color Channel for DCT Based Image
Watermarking Scheme”, International Journal of Security and its Applications. ISSN: 1738-9976, vol. 1, no. 2, pp. 41-46, Oct 2007.
http://www.sersc.org/journals/IJSIA/vol1_no2_2007.php [3] Saxena V., Gupta J.P., “A Novel Collusion Attack Resistant Watermarking Scheme
for Color Images”, IAENG International Journal of Computer Science, vol. 34, no. 2, ISSN: 1819-656X, pp. 171-177, Dec. 2007.
http://www.iaeng.org/IJCS/issues_v34/issue_2/index.html [4] Gupta A., Saxena V., Srivastava M.C, Gupta J.P., “Towards Achieving the Higher
Compression of Images Using Standard JPEG Scheme", [Accepted for publication in “International Journal of Tomography & Statistics”, ISSN 0972-9976 , June 2008 issue], Editor and reviewers comments received.
[5] Saxena V., Gupta J.P., “Collusion Attack Resistant Watermarking Scheme for Images
Using DCT”, Published in the Proc. 15th IEEE Int. Conf. on Signal Processing and Communication Applications, Turkey, pp.1-4, June 2007. http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4298551&arnumber=4298813&count=322&index=261
[6] Saxena V., Gupta J.P., “Towards Increasing the Robustness of Image Watermarking
Scheme Against JPEG Compression”, Proc. IAENG Int. Conf. on Imaging Engineering, Hong Kong, vol. 2, ISBN: 978-988-98671-7-1, publisher: Newswood Ltd, pp. 1903-1906, Mar., 2007.
[7] Saxena V., Gupta J.P., “Towards Increasing the Robustness of Image Watermarking
Scheme Against Histogram Equalization Attack”, Published in the Proc. 15th IEEE Int. Conf. on Signal Processing and Communication Applications, Turkey, pp. 1-4 June 2007. http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4298551&arnumber=4298829&count=322&index=277
148
Synopsis-1
SYNOPSIS
One of the biggest technological events of the last two decades was the invasion of digital
media in an entire range of everyday life aspects. Digital data can be stored efficiently and
with a very high quality, and it can be manipulated very easily using computers.
Furthermore, digital data can be transmitted in a fast and inexpensive way through data
communication networks without losing quality. Digital media offer several distinct
advantages over analog media. The quality of digital audio, images and video signals are
better than that of their analog counterparts. Editing is easy because one can access the exact
discrete locations that need to be changed. Copying is simple with no loss of fidelity and a
copy of a digital media is identical to the original. With digital multimedia distribution over
World Wide Web, Intellectual Property Right (IPR) are more threatened than ever due to the
possibility of unlimited copying . One solution would be to restrict access to the data using
some encryption technique. However encryption does not provide overall protection. Once
the encrypted data are decrypted, they can be freely distributed or manipulated. The above
problem can be solved by hiding some ownership data into the multimedia data, which can be
extracted later to prove the ownership. This idea is implemented in bank currency notes. In
bank currency notes, a watermark is embedded which is used to check the originality of the
note. The same “watermarking” concept may be used in multimedia digital contents for
checking the authenticity of the original content. So, A Watermarking is adding an
“ownership” information in multimedia contents to prove the authenticity. This
technology embeds a data, an unperceivable digital code, namely the watermark, carrying
information about the copyright status of the work to be protected. Continuous efforts are
being made to device an efficient watermarking schema but techniques proposed so far do
not seem to be robust to all possible attacks and multimedia data processing operations.
Considering the enormous financial implications of copyright protection, there is a need to
establish a globally accepted watermarking technique. The sudden increase in watermarking
interest is most likely due to the increase in concern over IPR. Today, digital data security
covers such topics as access control, authentication, and copyright protection for still images,
audio, video, and multimedia products. A pirate tries either to remove a watermark to
Synopsis-2
violate a copyright or to cast the same watermark, after altering the data, to forge the proof of
authenticity. Generally, the watermarking of still image, video, and audio demonstrate
certain common fundamental concepts. Numerous watermarking applications reported in the
literature depend on the services we wish to support. Thus watermarking techniques may be
relevant in various application areas including Copyright protection, Copy protection,
Temper detection, Fingerprinting etc .
Based on their embedding domain, watermarking schemes can be classified either as Spatial
Domain (The watermarking system directly alters the main data elements, like pixels in an
image, to hide the watermark data) or Transformed Domain (the watermarking system alters
the frequency transforms of data elements to hide the watermark data). The latter has proved
to be more robust than the spatial domain watermarking.
To transfer an image to its frequency representation, one can use several reversible
transforms like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), or
Discrete Fourier Transform (DFT). Each of these transforms has its own characteristics and
represents the image in different ways. Watermarks can be embedded within images by
modifying these values, i.e. the transform domain coefficients. In case of spatial domain,
simple watermarks could be embedded in the images by modifying the pixel values or the
least significant bit (LSB) values. However, more robust watermarks could be embedded in
the transform domain of images by modifying the transform domain coefficients. In 1997
Cox et.al presented a paper “Secure Spread Spectrum Watermarking for Multimedia” , one of
the most cited paper (cited 2985 times till April’ 2008 as per Google Scholar search), and
after that most of the research efforts are based on this work. Even though spatial domain
based techniques can not sustain most of the common attacks like compression, high pass or
low pass filtering etc., researchers present spatial domain based techniques too.
Since, financial implications of some of the application areas like fingerprinting and
copyright protection are very high and till now no successful algorithm seem to be available
to prevent illegal copying of the multimedia contents, the primary goal of this thesis work is
chosen to develop watermarking schemes for images (which are stored in spatial domain as
Synopsis-3
well as transformed domain) which can sustain the known attacks and various image
manipulation operations. This thesis resolves the following issues:
ISSUE 1: Till now there in no “Generic” nature in the watermarking algorithms available.
More precisely, if certain approach is applicable for a gray level image, the same approach
does not work for the other formats of an image.
ISSUE 2: Even if gray color image watermarking algorithms are extended for RGB color
images, the maximum work has been done for BLUE color channel only because human eyes
are less sensitive to detect the changes in BLUE color channel. No attack impact analysis,
i.e, which color channel may be affected by a particular attack, has been carried out.
Therefore, apart from choosing digital Image Watermarking as a major problem, we have
chosen to identify the suitability of a color channel with respect to attack (if any) for multi-
color channel images (True color windows BMP, uncompressed JPEG). We also decided to
explore the ways such that attack impacts may be minimized before the watermark
embedding process.
ISSUE 3: In most of the research papers, once the watermarking scheme is finalized, it is
applied to all test images. Since each image is different and has certain characteristics and
after embedding the watermark data by a particular watermarking scheme, its performance
against a particular attack may not be similar with other image. No study is conducted to
make the embedding scheme based on some image characteristics.
Therefore we have decided to explore the relationship between the performance of
watermarking scheme and the cover image characteristics itself.
ISSUE 4: Mostly watermarking schemes are developed in a way that first a scheme is
developed based on the extension of earlier presented one and then check its performance
against the common image manipulations and known attacks. There are huge financial
implications of watermarking schemes (say fingerprinting), but no scheme has been
Synopsis-4
developed, which is, by design, resistant to at least one attack, to ensure that, a particular
attack (having most financial issues) cannot be conducted by an attacker.
Therefore we decided to design watermarking schemes such that an inherent nature in can be
embedded to guarantee that at least one serious attack having most financial implications
cannot be conducted on watermarked images.
If owner identification applications place the same watermark in all copies of the same
content, then it may create a problem. If out of n number of legal buyer of content, one starts
to sell the contents illegally, it may be very difficult to know who is redistributing the
contents without permission. Allowing each copy distributed to be customized for each legal
recipient can solve this problem. This capability allows a unique watermark to be embedded
in each individual copy. Now if owner finds an illegal copy, he/she can find out who is
selling his contents by finding the watermark, which uniquely belongs to a singly legal buyer.
This particular application area is known as fingerprinting and thus has numerous financial
implications. The most serious attack for fingerprinting is the “collusion attack”. If attacker
has access to more than one copy of watermarked image, he/she can predict/ remove the
watermark data by colluding them. Researchers working on “fingerprinting” primarily focus
on the “collusion attack”.
So, while designing a watermark scheme, we decided that our proposed schemes must be
designed in such a way that schemes are inherently collusion attack resistant. Therefore this
thesis presents a new term “ICAR (Inherently Collusion Attack Resistant)” as a requirement
for a watermarking system. The other 3 issues are taken into account while developing the
watermarking schemes.
The first chapter is devoted to the introduction of the watermarking area. Data hiding
background is represented and the related terminologies are explained. Then various
application areas of watermarking are represented and what may the key requirements of a
successful watermarking system are discussed. Since watermarking can be classified on
Synopsis-5
various parameters, the various types of watermarking are represented based on different
classifications. The chapter-wise organization of the thesis is described.
The purpose of chapter-2 is to provide an overview of the existing watermarking techniques
and related emerging issues and then problem statement formulation based on the current
demand of the technology. In this chapter, apart from giving more emphasis only to those
papers, which are related to this thesis work, care has been taken to cover more and more
upcoming concepts. After then the shortcomings and the opportunities for the research work
are identified and based on those the research issues are developed by giving proper
justifications.
Chapter 3 describes the concepts like JPEG compression, which are the preliminaries
requirements. We are using Peak Signal to Noise ratio (PSNR) and Correlation coefficient
(CC) to measure the quality of the watermarked images and the extracted watermark logo,
which are also described in this chapter. Finally the test images (both stored in spatial and
transformed domain, gray and full colored) used in this thesis are given.
Our research work description starts from chapter 4. This chapter deals the watermarking of
the gray images. To start with, initially we focus how we can increase the robustness of the
well-known DCT and DWT based watermarking algorithms against some specific attacks.
We present a new concept of “preprocessing” to increase the PSNR value of extracted logo
from watermarked image if watermarked image has been attacked by JPEG compression
attack. Preprocessing steps change or modify the original image such that, the affect after the
attack on the watermarked image could be minimized. We tried to accomplish this by
creating the same effect in an image, before watermarking it, which this image shall have,
after it has been attacked. It is found that preprocessing steps increase the robustness of the
watermarking scheme. Since DCT based schemes are robust against those attacks, which do
not alter the perceptual quality of the image, we tested the proposed concept in the case of
such attack, which has serious impact on the perceptual quality of the image. Therefore, we
have extended the same hypothesis to increase the robustness against “Histogram
equalization” attack, which attacks on perceptual quality of the image. Our results favor the
Synopsis-6
proposed hypothesis and show the importance of the attack impact analysis to increase the
performance of the watermarking algorithms. After this, a watermarking scheme for gray
level images is developed which is ICAR in nature as well as very robust against common
image manipulation and attack (specially JPEG compression attack). The proposed scheme
is developed over the classical middle-band coefficient exchange scheme to inherit its
robustness against JPEG compression because this scheme takes the advantage of Human
Visual System (HVS). We achieve ICAR nature using randomness and redundancy in
coefficient exchange criterion. Apart from this, coefficient exchange criterion is develop to
be dependent on low frequency coefficients to provide extra robustness because we know that
any kind of attack or image manipulation can not alter the low frequency coefficients as this
will have a serious impact on the image quality. Results indicate that, this scheme is, not
only collusion attack resistant and resistant to common image manipulations and attacks, but
more robust against JPEG compression attack as compared to other similar, state-of-art,
watermarking schemes.
Chapter-5 deals with the watermarking of colored images. Colored images contain three
color channel (red, green and blue), and human eyes are least sensitive to detect the changes
in blue color channel and therefore most of the research work is based on hiding the
watermark data in blue color channel. We propose that the suitability of the color channel is
also dependent on the attack, the watermarked images have to undergo. For this we use 4 test
images in Window’s 24-bit image format and analyze the robustness against JPEG
compression attack by hiding the watermark data in all color channels. Results indicate that
there is a strong connection between the color channel selection and the robustness against
certain attack. It is found that for DCT based watermarking scheme, if we hide the
watermark data in green channel, the robustness against the JPEG compression increases.
The idea of preprocessing (proposed in previous chapter for gray level images) is also
verified for color images. We then developed an ICAR watermarking scheme for colored
images also, based on the scheme developed in chapter-4. We discovered that even after
some serious attacks, one cannot change the average of all middle band coefficients of 8x8
DCT. We used this fact in hiding the watermark data. Again, being an ICAR scheme, this
scheme is collusion attack resistant as well as very robust to common image manipulation.
Synopsis-7
We have tested test images against uniform scaling, brightness adjustment, Gaussion
blurring, Hue and saturation along with malicious attack like histogram equalization and
adding Gaussion noise. The proposed watermarking scheme sustains all attacks. In case of
performance against JPEG compression, proposed scheme outperforms other similar
watermarking schemes by giving very good results even at JPEG quality factor Q=5
(Compression ratio 98% and more), whereas other state-of-art watermarking schemes start
loosing its robustness below the JPEG quality factor Q=20.
Chapter-6 describes the watermarking of JPEG image. Since, most of the images present on
World Wide Web are in JPEG format, which is a highly compressed image format and stores
the images in the transformed domain, we developed an ICAR watermarking scheme for
JPEG images also.
Since JPEG is a very high compressed format, we know that while processing and storing a
JPEG image, lot of its coefficients will change their values and thus recovery of the
watermark data is difficult if only the relative strengths of coefficients of middle band regions
are considered. Therefore, we provide extra robustness (by involving some coefficients
whose values don’t changes much) by incorporating the large value at the top-left corner, the
DC coefficient in 8x8 block DCT. This DC coefficient is the major dominating value while
decompressing. This DC value alone can regenerate a very good approximated image by
taking the IDCT. If this value is altered, the image is largely affected. We hide the
watermark data based on DC coefficient. Proposed scheme is not only an ICAR scheme, but
also enhances the performance. Results indicated that, the proposed scheme recovers the
watermark even from highly attacked images which are compressed up to quality factor Q=5
of JPEG. In addition to this, the proposed scheme is resisting common image manipulations
like cropping, scaling, flipping, histogram equalization, brightness- contrast adjustment, Hue-
saturation alteration and Gaussion noise. In this chapter, we also explore a relationship
between the robustness and some image characteristics. We experiment the standard
deviation of an image and related this measure with the performance of the watermarking
scheme.
Synopsis-8
After successfully developing the ICAR watermarking schemes for gray, colored BMP image
and JPEG images using DCT, we explored the DWT. The basic need behind this is the
upcoming JPEG2000 format. This image format stores the image using wavelet transform.
Any of the image watermarked using our proposed algorithm may have to undergo
JPEG2000 format conversion also, therefore to make the watermark embedding liner to the
possible attack, we decided to use the DWT as embedding domain. A DWT based
watermarking scheme may not sustain those attacks which a DCT based scheme can sustain
very well (like JPEG compression), therefore we used the idea of Dual Watermarking, ie
embedding the watermark using both DWT and DCT to increase to number of possible
attacks which our watermarking scheme could sustain. Like our earlier proposed
watermarking schemes, apart from ICAR in nature, the proposed DWT based watermarking
scheme is very robust against .jp2 conversion attack (JPEG2000 format), JPEG compression,
and other common image manipulations and attacks.
In Chapter-7, summary of the results and goal achieved, are given in detail. Future work of
research work is also discussed. In the end, a list of all publications referred is given.
Keywords: Intellectual Property Right, Digital Image Watermarking, Collusion attack,
Discrete Cosine Transform, Discrete Wavelet Transform, Haar wavelet, JPEG image
encoding, Peak Signal to Noise Ratio, Correlation coefficient, JPEG2000 image
encoding.
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