NITHYA T Register No: 14MAE011 · Certified that this project report titled “DIGITAL WATERMARKING SYSTEM FOR VIDEO AUTHENTICATION” is the bonafide work of NITHYA.T [Reg ... Watermarking

DIGITAL WATERMARKING SYSTEM

FOR VIDEO AUTHENTICATION

PROJECT REPORT

PHASE II

Submitted by

NITHYA T

Register No: 14MAE011

in partial fulfilment for the requirement of award of the degree

Of

MASTER OF ENGINEERING

In

APPLIED ELECTRONICS

Department of Electronics and Communication Engineering

KUMARAGURU COLLEGE OF TECHNOLOGY

(An autonomous institution affiliated to Anna University, Chennai)

COIMBATORE - 641 049

ANNA UNIVERSITY: CHENNAI 600 025

APRIL - 2016

i

BONAFIDE CERTIFICATE

Certified that this project report titled “DIGITAL WATERMARKING SYSTEM FOR

VIDEO AUTHENTICATION” is the bonafide work of NITHYA.T [Reg. No.

14MAE011] who carried out the research under my supervision. Certified further that, to the

best of my knowledge the work reported herein does not form part of any other project or

dissertation on the basis of which a degree or award was conferred on an earlier occasion on

this or any other candidate.

The candidate with university Register No.14MAE011 is examined by us in the

project viva-voce examination held on............................

INTERNAL EXAMINER EXTERNAL EXAMINER

SIGNATURE

Mrs.A.KIRTHIKA

PROJECT SUPERVISOR

Department of ECE

Kumaraguru College of Technology

Coimbatore-641 049

SIGNATURE

Dr. A. VASUKI

HEAD OF THE DEPARTMENT

Department of ECE

Kumaraguru College of Technology

Coimbatore-641 049

ii

ABSTRACT

Everyday very huge amount of data is embedded on digital media or distributed

over the internet. The data so distributed can easily be replicated without error, putting

the rights of their owners at risk. Even when encrypted for distribution, data can easily

be decrypted and copied. One way to discourage illegal duplication is to insert

information known as watermark, into potentially vulnerable data in such a way that it

is impossible to separate the watermark from the data. These challenges motivated

researchers to carry out intense research in the field of watermarking. A watermark is

a form, image or text that is impressed onto paper, which provides evidence of its

authenticity. Digital watermarking is an extension of the same concept. There are two

types of watermarks: visible watermark and invisible watermark.

This project concentrates on implementing watermark in video. The main

consideration for any watermarking scheme is its robustness to various attacks.

Watermarking dependency on the original image increases its robustness but at the

same time watermark should be made imperceptible. In this project, a robust video

watermarking scheme using discrete wavelet transform (DWT) domain is proposed.

The quality of the watermarked video is enhanced by using wavelet transform.

Experimental results demonstrate that it is robust by calculating the peak signal to

noise ratio (PSNR) between the watermark image and extracted image.

Keywords: Video Watermarking, Wavelet Transformation, Discrete Wavelet

Transformation, Robustness, PSNR.

iii

ACKNOWLEDGEMENT

First I would like to express my praise and gratitude to the Lord, who

has showered his grace and blessing enabling me to complete this project in

an excellent manner. He has made all things in beautiful in his time.

I express my sincere thanks to our beloved Joint Correspondent,

Shri. Shankar Vanavarayar for his kind support and for providing

necessary facilities to carry out the project work.

I would like to express my sincere thanks to our beloved Principal

Dr.R.S.Kumar M.E., Ph.D., who encouraged us with his valuable thoughts.

I would like to express my sincere thanks and deep sense of gratitude to

our HOD, Dr.A.Vasuki M.E., Ph.D., for her valuable suggestions and

encouragement which paved way for the successful completion of the project.

I am greatly privileged to express my deep sense of gratitude to the

Project Coordinator Mrs.S.Umamaheswari M.E., (Ph.D)., Associate

Professor, for her continuous support throughout the course.

In particular, I wish to thank and express my everlasting gratitude to the

Supervisor Mrs.A.Kirthika M.E., (Ph.D), Assistant Professor-II for her

expert counselling in each and every steps of project work and I wish to

convey my deep sense of gratitude to all teaching and non-teaching staff

members of ECE Department for their help and cooperation.

Finally, I thank my parents and my family members for giving me the

moral support in all of my activities and my dear friends who helped me to

endure my difficult times with their unfailing support and warm wishes.

iv

TABLE OF CONTENTS

CHAPTER

NO.

TITLE PAGE NO.

ABSTRACT ii

LIST OF FIGURES vi

1.

INTRODUCTION 1

1.1 Information Hiding Technique 1

1.1.1 Steganography 2

1.1.2 Cryptography 2

1.1.3 Watermarking 2

1.2 Classification Of Watermarking Schemes 3

1.1.4 Robust Watermarking Schemes 3

1.1.5 Fragile Watermarking 5

1.1.6 Fragile Watermarking Schemes 5

1.3 Compression Techniques 6

1.3.1 Lossless Compression 7

1.3.2 Lossy Compression 7

1.3.3 Need For Compression 7

1.3.4 Compression Ratio 7

1.4 Need For Watermarking 7

1.5 Video Watermarking 7

1.6 Embedding Techniques 8

1.6.1 Spatial Domain Watermarking 9

1.6.2 Transform Domain Watermarking 9

1.6.3 Transform Domain Techniques 9

1.7 Requirements Of Digital Watermarking Scheme 9

1.8 Application Of Watermarking 10

2. LITERATURE SURVEY 12

v

3. METHODOLOGY 19

3.1Discrete Cosine Transform 19

3.1.1 Video Compressor Unit 19

3.1.2 Watermark Generator 21

3.1.3 Watermark Embedding Process 23

3.1.4 Comparison Between DCT & DWT 24

4 DISCRETE WAVELET TRANSFORM 25

4.1 Watermark Embedding Process 26

4.2 Watermark Extraction Process 27

4.3 Performance Metrics For Watermarking 28

4.3.1 Peak Signal To Noise Ratio 28

4.3.2 Mean Square Error 29

4.3.3 Structural Similarity Index 29

4.3.4 Normalised Correlation Factor 30

5. RESULTS AND DISCUSSIONS 31

5.1 Comparisons of PSNR, MSE,NC,SSIM for

Different Frames

37

6. CONCLUSION AND FUTURE WORK 41

7. REFERENCES 42

8. PUBLICATIONS 43

vi

LIST OF FIGURES

TABLE NO. NAME PAGE NO.

1.1 Overview Of Information Hiding

Techniques.

1

1.2 General Block Diagram Of System

3

1.3 Classification Of Embedding

Techniques

8

3.1 Block Diagram Of DPCM/DCT Coding

Scheme

21

3.2 Watermark Generator System 22

3.3 Block Diagram Of Watermark

Embedding Process

23

4.1 DWT Decomposition Levels 25

4.2 Watermark Embedding Process Using

DWT

26

4.3 Watermark Extraction Process Using

DWT

27

5.1 Original Input Video Frame 31

5.1 (a) Original Input Video Frames 1-81 32

5.1 (b) Original Input Video Frames 91-181 33

5.1 (c)

Original Input Video Frames 191-201

34

vii

TABLE

NO.

NAME PAGE

NO.

5.2 Watermark Image 34

5.3(A) Watermarked Video Frames 1-21 35

5.3(B) Watermarked Video Frames 31-111 35

5.3(C) Watermarked Video Frames 121-201 36

5.4 Extracted Watermark Image 37

viii

LIST OF ABBREVATIONS

DCT Discrete Cosine Transform

DWT Discrete Wavelet Transform

JND Just Noticeable Distortion

HVS Human Visual System

VW2D Variable-Watermark Two-Dimensional Algorithm

LSB Least Significant Bit

DPCM Differential Pulse Code Modulation

RNG Random Number Generator

GOP Group Of Pixel

PSNR Peak Signal To Noise Ratio

NC Normalised Correlation

SSIM Structural Similarity Index Matrix

MSE Mean Square Error

1

CHAPTER 1

INTRODUCTION

Because of the fast and extensive growth of network technology, digital

information can be distributed with no quality loss, low cost and nearly instantaneous

delivery. Protection of multimedia content has recently become an important issue

because of the consumers' insufficient cognizance of the ownership of intellectual

property. Thus, over the past several decades, digital information science has emerged

to seek answers to the question: can researchers ensure tamper-resistance and protect

the copyright of digital contents by storing, transmitting, and processing information

encoded in systems where digital content can easily be disseminated through

communication channels? Today it is understood that the answer is yes, and many

research groups around the world are working towards the highly ambitious

technological goal of protecting the ownership of digital contents, which would

dramatically protect inventions represented in digital form for being vulnerable to

illegal possession, duplication and dissemination [3].

1.1 INFORMATION HIDING TECHNIQUES:

Fig 1.1 Overview of Information Hiding Techniques

Information

hiding

technique

Steganography

Cryptography

Watermarking

2

1.1.1 STEGANOGRAPHY:

Steganography is the hiding of a message within image so that the presence of

the hidden message is indiscernible. The key concept behind steganography is that the

message to be transmitted is not detectable to the casual eye. In fact, the people who

are not intended to be recipients of the message should not even suspect that a hidden

message exists.

1.1.2 CRYPTOGRAPGHY:

Cryptography is about constructing and analysing protocols that prevent third

parties or the public from reading private messages. It is , then, not only protects data

from theft or alteration, but can also be used for user authentication. There are, in

general, three types of cryptographic schemes typically used to accomplish these

goals: secret key (or symmetric) cryptography, public-key (or asymmetric)

cryptography, and hash functions, each of which is described below. In all cases, the

initial unencrypted data is referred to as plaintext. It is encrypted into cipher text,

which will in turn (usually) be decrypted into usable plaintext.

1.1.3 WATERMARKING:

Digital watermarking is the process of embedding or hiding digital information

called watermark into a multimedia product, and then the embedded data can later be

extracted or detected from the watermarked product, for protecting digital content

copyright and ensuring tamper-resistance, which is indiscernible and hard to remove

by unauthorized persons. A host signal is a raw digital audio, image, or video signal

that will be used to contain a watermark [2]. A watermark itself is loosely defined as a

set of data, usually in binary form, that will be stored or transmitted through a host

signal. The watermark may be as small as a single bit, or as large as the number of

samples in the host signal itself. It may be a copyright notice, a secret message, or any

other information. After adding the watermark in the original image, there should be

no image degradation, watermark should not be removable and should be robust

against different types of attacks.

3

Fig 1.2 General block diagram of watermarking system

1.2 CLASSIFICATION OF WATERMARKING SCHEMES:

Digital watermarking schemes can be broadly classified into four categories,

namely, Robust, Fragile, Semi-fragile and Reversible. While, as mentioned

previously, imperceptibility, low embedding distortion and security are the common

requirements of all classes, each different category of scheme has different

characteristics and, thus, is suitable for different applications. For example, while

robustness is an essential requirement for copyright applications, it has no role in most

authentication applications. This section provides a brief explanation of each of these

schemes along with application areas where they can be applied [4].

1.2.1 Robust Watermarking Schemes

A robust watermarking system is resilient against wide range of intentional and

unintentional image processing operations such as image enhancement, altering, noise

addition, JPEG compression and geometrical transformations, collusion and forgery

attacks.

(a) Spread Spectrum based Robust Watermarking

The spread spectrum communications embed information using small amount

of energy with large spectrum. In each band, the corresponding information or energy

4

becomes very small and undetectable. Thus, it is difficult to remove the signal

(watermark) from the host signal (cover content) if spread spectrum communication is

applied. This approach uses spread spectrum communication techniques embed a

single bit in the image [6]. spread spectrum communication can be defined as :

"Spread spectrum is a means of transmission in which the signal occupies a band

width in excess of minimum necessary to send the information, the band spread is

accompanied by a code which is independent of the data, and a synchronized

reception with the code at the receiver is used for dispreading and subsequent data

recovery".

(b) JND Model based Robust Watermarking

In this method to embed the amount of modification on image which will not

be aware by human perception as JND (Just Noticeable distortion). This model is

tested in both DCT and DWT domains and the result indicated that the manipulation is

not noticed by human eyes. JND model or HVS (Human Visual System) are

subjective measure of transparency. The masking effect is the minimum level below

which a signal cannot be aware, in DCT domain. Using the masking effect, the

watermark can be embedded into an image in a manner such that human eyes cannot

perceive.

(c) Spatial Domain based Robust Watermarking

In this technique modification of random selected pixels is performed and hypothesis

testing is used to detect the Watermark. This approach is robust to JPEG compression

and low passes filtering.

(d) Channel State Estimation based Robust Watermarking

This method proposes the scenario of optimal watermark and extraction. The

watermark problem is treated as communication with side information. The side

information includes secret key and channel state information such as cover image and

attack channel. According to the combination of whether the side information is

available on watermark embedding and watermark extraction. It can be concluded that

the optimal watermarking system should take into consideration all available side

information at both watermark embedding and watermark extraction.

5

1.2.2 Fragile Watermarking

A fragile watermark can be destroyed easily. This property is useful to identify

whether a multimedia is modified or not. By modulating fragile watermark into

multimedia, the authenticity of multimedia can be authenticated. Any modification on

the multimedia will make the corresponding embedded fragile watermark destroyed.

By examining a fragile watermark, the position where the modification occurred can

be identified easily.

1.2.3 Fragile Watermarking Methods

(a) Quantization-based Fragile Watermark

In this technique by examining the destroyed fragile watermark, the position

where malicious modification occurred could be identified. This technique identifies

the type of incidental distortion as JPEG compression, if the ratio of the number of

destroyed watermark over the number of all watermark decrease from high resolution

to low resolution in wavelet transform. However, this approach cannot identify the

type of modification if both an instance of malicious tampering and an incidental

distortion are simultaneously applied.

(b) Block Hashing

In the first variance of the approach, hash function is applied on blocks of

image. Any modification on this protected image will vary the value of the hash

function. Thus, the area which is tampered with can be identified. In second approach,

they examined the Variable-Watermark Two-Dimensional Algorithm (VW2D). The

VW2D technique use the stored values obtained watermark and the watermarked

image to perform image authentication on a block-by-block basis. Both of these two

examined methods need store values for further processing. There is an extra need of

management of these stored data.

1.2.4 Semi-Fragile Watermarking Schemes

To facilitate the authentication and content-integrity verification for multimedia

applications where content preserving operations are a common practice, semi-fragile

watermarking scheme have been proposed in the last few years. This class of

6

watermarks is intended to be fragile only when the manipulations on the watermarked

media are deemed malicious by the schemes. Usually, to achieve semi-fragility, the

schemes exploit properties of, or relationships among, transformed coefficients of the

media. Such properties and relationships are invariant to content-preserving operations

while variant to malicious manipulations. The watermark is embedded by quantizing

or adjusting the coefficients according to the watermark [1].

The defined quantization step governs the fragility or sensitivity to

manipulations and the degree of distortion. However, an immediate result of

coefficient quantization is that a unique watermark may be extracted from many

different media, which might have been subjected to some forms of content-

preserving operations or malicious manipulations. Such a one-to-many

correspondence can be problematic in terms false positives (i.e. a watermark, that was

never embedded, is detected by the detector) and false negatives (i.e. the detector fails

to detect an embedded watermark). Unfortunately, no optimal criteria for maintaining

low false positive and false negative rates are currently in existence. Another

challenge semi-fragile schemes faces is how to distinguish content-preserving

operations from malicious attacks. For example, transcoding may be deemed

acceptable for one application while it may be seen as malicious for another.

Therefore, with these two issues, semi-fragile watermarking is usually not suitable for

applications concerning legal and national security issues.

1.3 COMPRESSION TECHNIQUES:

Image compression is the representation of an image in digital form with as few

bits as possible while maintaining an acceptable level of image quality. Data

compression is the technique to reduce the redundancies in data representation in

order to decrease data storage requirements and hence communication costs. Reducing

the storage requirement is equivalent to increasing the capacity of the storage medium

increase the speed of transmission and hence communication bandwidth [3].

Lossless or lossy.

Symmetrical or asymmetrical.

Software or hardware.

7

1.3.1 LOSSLESS COMPRESSION

Here, compressed data can be used to recreate an exact replica of the original;

no information is lost to the compression process.

1.3.2 LOSSY COMPRESSION

In this, the original signal cannot be exactly reconstructed from the compressed

data. In lossy image compression, though very fine details of the images are lost, but

image size is drastically reduced.

1.3.3 NEED FOR COMPRESSION:

Reduce file size.

Save disk space.

Increase transfer speed at a given data rate.

1.3.4 COMPRESSION RATIO:

Data compression ratio is defined as the ratio between the uncompressed

size and compressed size.

Compression ratio =

1.4 NEED FOR WATERMARKING:

Protect Copyright Of A Data.

Video Watermarking Can Help To Prove Ownership

Identify A Misappropriating Person Trace The Video Dissemination

Video Watermarking Introduces Some Issues Not Presenting In Image

Watermarking Like Frame Averaging, Frame Dropping, Frame Swapping.

1.5 VIDEO WATERMARKING:

Video Watermarking is the process involved in embedding a watermark into

some cover data (video, audio, text etc.) for the purpose of identification of the owner

or original source of the multimedia data. In video watermarking a low-energy signal

is imperceptibly embedded in another signal. The low-energy signal is called

watermark and it depicts some metadata, like security or rights information about the

main signal.

8

The main signal in which the watermark is embedded is referred to as cover

signal since it covers the watermark. The cover signal is generally a still image, audio

clip, video sequence or a text documents in digital format.

1.6 EMBEDDING TECHNIQUES

Watermarks can be embedded in the pixel/spatial domain or in transform

domain. In spatial domain, the watermark is embedded directly by modifying the

intensity values of pixels. In frequency domain, the watermark is embedded by

changing the frequency coefficients. To transform video into frequency domain, the

transformation techniques such as Discrete Wavelet Transform (DWT), Discrete

Cosine Transform (DCT), Discrete Hadamard Transform and Discrete Fourier

Transform are used. Spatial domain watermarking technique is easier and its

computing speed is high, than transform domain watermarking. But the disadvantage

is that it is not robust against common video processing operations. Transform domain

techniques are introduced to increase the robustness of the digital media [9].

Fig 1.3 Classification of Embedding Techniques

9

1.6.1 SPATIAL DOMAIN WATERMARKING:

The most common implementation of spatial domain watermarking is Least

Significant Bit (LSB) replacement Method. It involves replacing the n least significant

bits of each pixel of a container image with the data of a hidden image

1.6.2 TRANSFORM DOMAIN WATERMARKING:

In this technique we apply some invertible transform to the host image before

embedding the watermark. Then the transform domain coefficients are modified to

embed the watermark and finally the inverse transform is applied to obtain the marked

image.

1.6.3 TRANSFORM DOMAIN TECHNIQUES:

a) Discrete Cosine Transform (DCT):

This is the most commonly used transform for watermarking purpose. The

DCT allows an image to be broken up into different frequency bands making it much

easier to embed watermarking information.

b) Discrete Wavelet transforms (DWT):

This technique is also called as multiresolution technique. The important aspect

of this technique is that watermark is introduced in imperceptibly significant regions

of the data in order to remain robust.

1.7 REQUIREMENTS OF DIGITAL WATERMARKING SCHEME:

Generally, a practical watermarking system embeds some copyright

information into the host data as a proof of rightful ownership and must meet

requirements. Obviously, different applications have different requirements for

watermarking system. Therefore, it is quite difficult to have a unique set of

requirements that all watermarking system must satisfy. The requirements with

respect to copyright protection and rightful ownership are as follow:

a) Robustness: Robustness refers to the ability of the watermark to be preserved

even after distortions introduced by standard or malicious data processing,

10

which may be either intentionally or un-intentionally. These distortions are also

known as watermarking attacks.

b) Imperceptibility:

The imperceptibility of the watermark refers to its perceptual transparency.

In other words, the human eye should not be able to detect differences

between the watermarked and original video.

c) Capacity:

Capacity refers to the maximum amount of information that can be hidden

in the media. This directly affects the robustness and perceptual

transparency.

d) Security:

Security refers to the neither fact that unauthorized persons should neither

detect nor read the watermark; however, it must be retrieved correctly by

the authorized user.

1.8 CLASSIFICATION OF WATERMARKS:

Visible

Invisible.

In visible watermarking, the information is visible in the video. While in invisible

watermarking, information is not visible. It can be detected only by owner.

1.9 APPLICATIONS OF DIGITAL WATERMARKING:

Digital watermarking is well entrenched research area with plenty of

applications. The major applications of digital video watermarking includes digital

copyright protection, video authentication, broadcast Synchronization System, copy

control, fingerprinting, tamper resistance, video tagging, ownership identification and

enhance video coding [10].

Broadcast Monitoring Watermarking is obviously a suitable technique for

information monitoring. This has major application is commercial advertisement

broadcasting where the entity who is advertising wants to monitor whether their

advertisement was actually broadcasted at the right time and for right duration. The

11

watermark exists within the content and is compatible with the installed base of

broadcast equipment. The watermarks can automatically be extracted to verify if a

commercial has successfully been aired or whether a certain segment of material was

used in a broadcast. The content is usually watermarked by the content owner, while

detection can be done by a monitoring site in the broadcast chain or a third party at the

receiving end.

Content Authentication is a method that attempts to ensure the integrity of media by

detecting attempted tampering of the original content. The content is usually

watermarked with a semi-fragile watermark, which is designed to be affected by

signal transformations. Tampering with the content should destroy or alter this semi-

fragile watermark, which could then be used to determine that the content is not

authentic.

Digital Fingerprinting is a technique used to detect the owner of the digital content.

Fingerprints are unique to the owner of the digital data. Hence a single digital content

can have different fingerprints because they related to different users.

Tamper Detection When database content is used for very critical applications such

as commercial transactions or medical applications, it is important to ensure that the

content was originated from a specific source and that it had not been changed,

manipulated or falsified. This can be achieved by embedding a watermark in the

underlying data of the database. Tamper detection is also useful in court of law where

digital images could be used as a forensic tool to prove whether the image is tampered

or not.

Copyright protection is a technique used to embed the ownership rights in a

multimedia work by its creators. Watermarking can be used to protecting

redistribution of copyrighted material over the untrusted network like Internet or peer-

to-peer (P2P) networks.

12

CHAPTER 2

LITERATURE SURVEY

[1] IMPLEMENTATION OF DIGITAL VIDEO WAYTERMARKING

SCHEME BASED ON FPGA

Prachi V. Powar, International Journal of Electrical, Electronics and Computer

Systems

An objective of this scheme is to develop low power, robust and secure

watermarking system for authentication of video. Here we present an FPGA based

implementation of an invisible watermarking encoder. It consists of a watermark

generator module and watermark insertion module. The system is initially simulated

and tested for various attacks in MATLAB/Simulink and then prototyped on

VERTEX-6 FPGA using VHDL. The watermarked video is same as that of original

video with an average Peak-Signal-to-Noise Ratio (PSNR) of 46 db.

[2] TRANSFORM DOMAIN VIDEO WATERMARKING DESIGN,

IMPLEMNTATION AND PERFORMANCE ANALYSIS

Ashish M. Kothari , IEEE

Transform domain method for the digital watermarking of video for embedding

invisible watermarks behind the video is discussed. It is used for the copyright

protection as well as proof of ownership. In this paper we have specifically used the

characteristics of 2-D Discrete wavelet Transform and discrete cosine transform for

the watermarking. In this work, we first extracted the frames from the video and then

used Frequency domain characteristics of the frames for watermarking. We calculated

different parameters for the sake of comparison between the two methods.

13

[3] A ROBUST QR-CODE VIDEO WATERMARKING SCHEME BASED ON

SVD AND DWAT.COMPOSITE DOMAIN

G Prbhakaran, R.Bhavani, M.Ramesh, Informatics and Mobile Engineering (PRIME)

IEEE .

A video watermarking with text data (verification message) by using the Quick

Response (QR) Code technique. A quick response (QR) code is a two dimensional

barcode invented by the Japanese corporation Denso Wave. The QR Code is

watermarked via a robust video watermarking scheme based on the (singular

Value decomposition) SVD and (Discrete Wavelet Transform) DWT. This method is

convenient, feasible and practically used for providing copyright protection. SVD is

an algebraic transform for watermarking applications. SVD is applied to the

cover I-frame. The extracted diagonal value is fused with logo (or) watermark. DWT

is applied on SVD cover image and QR code image. This method has achieved the

improved imperceptibility and security watermarking.

[4] RESEARCH ON VIDEO COPYRIGHT PROTECTION SYSTEM

Yujie Zhang, IEEE

A video watermarking algorithm in detail based on DCT, DWT and neural

network technology and digital watermarking was proposed and a professional video

copyright protection platform was built using the above algorithm. This algorithm

effectively enhances the robustness of the video stream. The platform includes video

watermark embedding, watermark detection and video piracy tracking and other

functions. It doesn’t only achieve the prevention beforehand but also the piracy

tracking afterwards. The simulation results show that the platform can effectively

implement the copyright protection of digital video works.

[5] BLOCK BASED VIDEO WATERMARKING SCHEME USING WAVELET

TRANSFORM AND PRINCIPLE COMPONENT ANALYSIS

Nisreen I. Yassis, International Journal of Computer Science Issues

A comprehensive approach for digital video watermarking is introduced, where

a binary watermark image is embedded into the video frames. Each video frame is

14

decomposed into sub-images using 2 level discrete wavelet transform then the

Principle Component Analysis (PCA) transformation is applied for each block in the

two bands LL and HH. The watermark is embedded into the maximum coefficient of

the PCA block of the two bands. The proposed scheme is tested using a number of

video sequences. Experimental results show high imperceptibility where there is no

noticeable difference between the watermarked video frames and the original frames.

The computed PSNR achieves high score which is 44.097 db. The proposed scheme

shows high robustness against several attacks such as JPEG coding, Gaussian noise

addition, histogram equalization, gamma correction, and contrast adjustment.

[6] A ROBUST WATERMARKING SCEHEM FOR REGION OF INTERSET

IN H.264 SCALABLE VIDEO CODING

Bao, J., J. Guo, and J. Xu, Sensor Network and Automation (IMSNA), 2nd

International Symposium on. IEEE

A watermarking scheme based on Region of Interest (ROI) in H.264/SVC. The

watermark is embedded only in the ROI, where the most valuable contents

of the video such as moving objects are protected, and the background is not changed.

Thus, this technique enhances the robustness of the watermarking scheme against

some common attacks with less complexity.

[7] REAL TIME COPRESSED-DOMAIN VIDEO WATERMARKING

RESISTANCE TO GEOMETRIC DISTORTIONS

Wang, L., H. Ling, F. Zou, and Z. Lu IEEE Multimedia

The method based on two stages. Firstly, obtain one level of DWT on a block

of DCT coefficients for the compressed video. Secondly, embed the watermark into

histogram bins of frames in the one-level DWT domain. The video data is partially

decoded to obtain block discrete cosine transform (DCT) coefficients, which are

subsequently used to construct a one-level DWT. This method reduces the

computational cost and meets the real-time requirement in the compressed domain.

15

[8] A STUDY ON SPATIAL AND TRANSFORM DOMAIN

WATERMARKING TECHNIQUE

P. Dabas, K. Khanna, International journal of computer application

Digital image watermarking is used for copyright protection of digital

information, with the widespread of internet; the intellectual properties are accessible

and manipulated easily. It demanded to have different ways to protect data. Digital

watermarking provides a viable and promising solution. In this paper, we have

described about the three different watermarking techniques (LSB, DCT, DWT) along

with the various performance parameters required to evaluate the best technique out of

them. This can help us to propose and implement new technique to achieve maximum

robustness against various attacks.

[9] A STUDY OF DIGITAL IMAGE WATERMARKING

M. kaur, S. Jindal, S. Behal ,IEEE

The gap in Frequency domain and Spatial-domain methods, frequency-domain

methods are more widely applied than spatial domain. The intent is to embed the

watermarks in the spectral coefficients of the image. The most commonly used

transforms are the Discrete Cosine Transform (DCT), Discrete Fourier Transform

(DFT), Discrete Wavelet Transform (DWT), the reason for watermarking in the

frequency domain is that the characteristics of the human visual system (HVS) are

better captured by the spectral coefficients.

[10] SECURITY IN MEDIACL IMAGE COMMUNICATION WITH

ARNOLD’S CAT MAP METHOD AND REVERSIBLE WATERMARKING

A.Umamageswari, G.R.Suresh, International Conference on Circuits, Power and

Computing Technologies

A reversible watermarking technique to embed information into medical

images. In this paper Region of interest (ROI) and Region of non interest (RONI) is

defined. ROI is protected and effort is made to embed data in RONI. When medical

image shared through network, for the compression purpose the JPEG2000 algorithm

is proposed and to improve the information security to maintain the secrecy, reliability

16

and accessibility of the embedded data Arnold’s cat map method (Arnold Transform)

is proposed. Patient information and disease information is embedded into

DICOM images. Increase in authentication can be achieved using Kerberos technique.

[11] A CHAOS-BASED ROBUST WAVELET-DOMAIN WATERMARKING

ALGORITHM.

Zhao Dawei, Chen Guanrong, Liu Wenbo. Chaos, Solitons and Francrals 2004;

22:47-54.

Dawei Zhao proposed a novel scheme of image watermarking. This scheme

applied the wavelet transform locally, based on the chaotic logistic map, and

embedded the watermark into the DWT domain. The watermark was detected by

computing the correlation between the watermarked coefficients and the

watermarking signal, where the watermarking threshold was chosen according to the

Neyman-Pearson criterion based on some statistical assumption. It is a very promising

technique for high-quality and reliable watermarking applications.

[12] AN EFFICIENT FRACTAL IMAGE CODING ALGORITHM USING

UNIFIED FEATURE AND DCT.

Yi-Ming Zhou, Chao Zhang, Zeng-Ke Zhang.

YiMIng Zhou proposed a special unified feature and DCT coding algorithm to

improve the fractal image compression. Firstly, a special unified feature (UFT) was

used to reduce the search space obviously and exclude most inappropriate matching

sub-blocks. Secondly, in order to improve the quality of the reconstructed image, a

DCT coder was combined to construct a hybrid fractal image algorithm (DUFC). The

proposed algorithms could obtain good quality of the 64 reconstructed images And

need much less time than the baseline fractal coding algorithm.

17

[13] A NEW DCT - BASED WATERMARKING METHOD FOR COPYRIGHT

PROTECTION OF DIGITAL AUDIO

P. K. Dhar, M. I. Khan, S. Ahmad, International journal of computer science and

information technologies (IJCSIT), Vol.2, No. 5, 2010

Ahmad and P.K. Dhār suggested absolute values of DCT coefficients that are

divided into an arbitrary number of segments and the energy of each segment is

calculated. Watermarks are then embedded into the selected peaks of the highest

energy segment. Watermarks are extracted by performing the inverse operation of

watermark embedding process. Simulation results indicate that the proposed

watermarking method is highly robust against various kinds of attacks such as noise

addition, cropping, re-sampling, re-quantization, MP3 compression, and echo, and

achieves similarity values ranging from 13 to 32.

[14] A MULTI-BAND WAVELET WATERMARKING SCHEME

X. Kang ,W. Zeng ,and J. Huang International Journal of Network Security, Vol. 6 ,

No. 2, March 2008, pp. 121–126.

Kang has proposed a new algorithm where advantage of the strength of both

multi-band wavelets transform (MWT) and PCA is used. The watermark energy is

distributed to wavelet coefficients of every detail sub-band efficiently to achieve

better robustness and perceptual transparency.

[15] INFORMATION EMBEDDING AND AUTHENTICATION IN MEDICAL

IMAGES USING LEAST DIFFERENCE METHOD

Sulakshna, Sonia, International Journal Of Advanced Research In Electrical,

Electronics And Instrumentation Engineering Vol. 2, Issue 7, July2013

The author proposed a lossless semi-reversible watermarking technique for

DICOM images which works in semi reversible domain. This technique can embed

high capacity of textual data in an image in noisy pixels of the image. It uses the

technique of minimum value difference in characters integral value and the pixels of

the image and finds the matching on least difference basis. There is no overlapping in

the embedded data, hence full recovering of information at receivers end. It can be

18

used to hide patient's data hiding and protecting the region of interest (ROI) with

tamper detection and recovery capability. The experimental results show that the

original image can be exactly extracted from the watermarked one in case of no

tampering. In case of alterations, made in the transmitted medium by an unauthorized ,

it is able to detect and locate them in the images.

19

CHAPTER 3

METHODOLOGY

In general, digital WM techniques proposed so far for media authentication are

usually designed to be visible or invisible robust or invisible-fragile watermarks

according to the level of required robustness. Each of the schemes is equally important

due to its unique applications. In this project however, we present the implementation

of the invisible semi fragile watermarking system for video authentication. The

motivation here is to integrate the video watermarking system with a surveillance

video camera for real-time watermarking in the source end.

3.1 DISCRETE COSINE TRANSFORM

In general, for each DCT block of a video frame, N cells need to be identified

as“watermarkable” and modulated by the watermark sequence. The chosen cells

contain nonzero DCT coefficient values and are found in the mid-frequency range.

The watermark embedding approach is designed to be performed in the DCT domain.

This holds several advantages. DCT is used in the most popular stills and video

compression formats, including JPEG, MPEG, and H.26x. This allows the integration

of both watermarking and compression into a single system. Compression is divided

into three elementary phases: DCT transformation, quantization, and Huffman

encoding. Embedding the watermark after quantization makes the watermark robust to

the DCT compression with a quantization of equal or lower degree used during the

watermarking process. Another advantage of this approach is that in image or video

compression the image or frames are first divided into 8 × 8 blocks. By embedding the

WM specifically to each 8×8 block, tamper localization and better detection ratios are

achieved [11].

3.1.1 VIDEO COMPRESSION UNIT:

The popular standards for video compression, namely MPEG-x (ISO standard)

and H.26x formats (ITU-T standard), use the same basic hybrid coding schemes that

apply the principle of motion-compensated prediction and block based transform

20

coding using DCT. Generally, a video sequence is divided into multiple group of

pictures (GOP), representing sets of video frames which are neighbouring in display

order. An encoded MPEG-2 video sequence is made up of two frame-encoded

pictures: intraframes (I-frame) and interframes (P-frame or B-frame). P-frames are

forward prediction frames and B-frames are bidirectional prediction frames. Within a

typical sequence encoded GOP, P-frames may be 10% of the size of I-frames and B-

Frames are about 2% of the I-frames.

There can be two types of redundancies in video frames: temporal

redundancy and spatial redundancy. MPEG-2 video compression technique reduces

these redundancies to compress the images.

Within a GOP, the temporal redundancy among the video frames is reduced by

applying temporal differential pulse code modulation (DPCM). The major video

coding standards, such as H.261, H.263, MPEG-1, MPEG-2, MPEG-4, and H.264, are

all based on the hybrid DPCM/DCT CODEC, which incorporates motion estimation

and motion compensation function, a transform stage and an entropy encoder.

It has been illustrated in fig.3.1 that an input video frame is compared with

a reference frame (previously encoded) and a motion estimation function finds a

region in that t matches the current macro-block in . The offset between the

current macro-block position and the chosen reference region is a motion vector, dk.

Based on this dk, a motion compensated prediction is generated, and it is then

subtracted from the current macro-block to produce a residual or prediction error, e.

For proper decoding this motion vector, dk, has to be transmitted as well.

The spatial redundancy in the prediction error, e (also called the displaced

frame difference) of the predicted frames, and the I-frame is reduced by the following

operations: each frame is split into blocks of 8 × 8 pixels that are compressed using

the DCT followed by quantization (Q) and entropy coding (run-level-coding and

Huffman coding) [12].

21

Fig 3.1 Block diagram of DPCM/DCT coding scheme

3.1.2 WATERMARK GENERATOR:

Since simple watermark data can be easily cracked, it is essential that the

primitive watermark sequence will be encoded by an encipher. This insures that the

primitive watermark data are secured before being embedded into each video frame.

The WM generator generates a secure watermark sequence for each video frame using

a meaningful primitive watermark sequence and secret input keys. A primitive

watermark pattern can be defined as a meaningful identifying sequence for each video

frame.

The block diagram of the proposed novel watermark generator is depicted in

Fig 3.2. A secure watermark pattern is generated by performing expanding,

scrambling, and modulation on a primitive watermark sequence. There are two digital

secret keys: Key 1 is used for scrambling and Key 2 is used for the random number

generator (RNG) module that generates a pseudorandom sequence.

22

Fig.3.2 Watermark generator system

Initially, the primitive binary watermark sequence, (of 64 bit), is expanded

( ) and stored in a memory buffer. is expanded by a factor . Scrambling is actually

a sequence of XOR operations among the contents (bytes) of the expanded primitive

WM in the buffer. Key 1 initiates the scrambling process by specifying two different

addresses (Add1 and Add2) of the buffer for having the XOR operation in between

them.

The basic purpose of scrambling is to add complexity and encryption in the

primitive watermark structure. After that, the expanded and scrambled sequence is

obtained. The bit size of ci is the same as the size of the video of frame. Finally, the

expanded and scrambled watermark sequence , is modulated by a binary

pseudorandom sequence to generate the secured watermark sequence . Due to the

random nature of the pseudorandom sequence , modulation makes the watermark

sequence a pseudorandom sequence and thus difficult to detect, locate, and

manipulate. A secure pseudorandom sequence used for the modulation can be

generated by an RNG structure using the Key 2 [12].

23

3.1.3 WATERMARK EMBEDDING PROCESS:

Watermark embedding is done only in the I (intra) frames. It is understandable

since B and P frames are predicted from I frames If all I, B, P frames are

watermarked, the watermarked data of the previous frame and the one of the current

frame may accumulate, resulting in visual artifacts (called “drift” or “error

accumulation”) during decoding procedures. To avoid such a major issue, within each

GOP of MPEG-2 video stream, only the I-frame is identified to be watermarked.

Fig.3.3 Watermark embedding system

24

3.2 COMPARISON BETWEEN DCT AND DWT TECHNIQUES:

Methodology

Used

Existing Method Proposed Method

Discrete cosine transform

(DCT)

Discrete Wavelet Transform(DWT)

Advantages

It works only in the block

size of 8*8(M*N)

Better robustness against

certain attacks.

Lower compression ratio.

Allows good localization

both in time and spatial

frequency.

No need to divide the input

coding into non-

overlapping 2-Dblocks.

It has higher compression

ratio.

Avoids blocking artifacts.

Higher quality.

Higher performance.

Reduces Computation time

and resource required.

Disadvantages

Very low bit rates.

Poor performance.

Longer compression time.

Computational cost is high.

Table 3.4 Comparison between DCT and DWT Techniques

25

4. DISCRETE WAVELET TRANSFORM:

Mathematical Technique i.e. DWT is utilized in the algorithm watermarking. In

a wide variety of signal processing application discrete wavelet transform is used.

Decomposition of an image or a video frame into sub images is done by 3D DWT.

The sub image resembles the original on ¼ the scale of original during approximation.

Frequency Band of an image is separated into lower resolution approximation sub-

band, horizontal, vertical, diagonal detail components robustness increases with

respect to attacks by embedding the watermark in low frequencies. High frequency

sub-band is less sensitive to high frequencies; watermark becomes more imperceptible

due to embedding of watermark in high frequency sub-bands. While it becomes more

robust against variety of attacks such as filtering, lossy compression and geometric

distortion, due to embedding in low frequencies [14].

Fig 4.1 DWT Decomposition levels

Basically, four coefficient images are divided using discrete wavelet transform in the

single level. 3D DWT generates four coefficients: LL, LH, HL, and HH. Here HH is

diagonal high frequency band, HL is vertical high Frequency band, LH is horizontal

high frequency band and LL represents low frequency band. In high altitudes, the

most prominent information get appears likewise the less prominent information

appears in very low altitudes. Data compression can be achieved by discarding these

low altitudes. High compression ratio with good quality of reconstruction is enabled

26

by wavelet transform. As compared to FFT or DCT, DWT is believed to the more

accurate model of human visual system.

4.1 WATERMARK EMBEDDING PROCESS:

In this model user has to provide original image, watermark image and scaling

factor (α) as input for the embedding process. After that find out the wavelet

transforms of original image and choose “Haar” wavelet in the high frequency band.

During embedding process, embed the watermark coefficient with the highest value

wavelet coefficient of original image. Apply inverse DWT to the image and get

watermarked image. Firstly the gray scale host image is taken and 2-D, 3-level DWT

(Discrete Wavelet Transform) is applied to the image which decomposes image into

low frequency and high frequency components. In the same manner 2-D, 3-level

DWT is also applied to the watermark image which is to be embedded in the host

image. The technique used here for inserting the watermark is alpha blending. In this

technique the decomposed components of the host image and the watermark are

multiplied by a scaling factor and are added. Since the watermark embedded in this

paper is perceptible in nature or see able, it is implanted in the low frequency

estimation component of the host image.

Fig 4.2 Watermarking Embedding Process Using DWT

27

According to the formula of the alpha blending the watermarked image is granted by

LL2, LH2, HL2, and HH2.

WMI = K*(LL2) +*q (LL2)*kW

Where WMI = low frequency component of watermarked image, LL3 = low

frequency component of the original image obtained by 3-level DWT, WM3 = low

frequency component of Watermark image, and k, q = Scaling factors for the original

image and watermark respectively. After embedding the cover image with watermark

image, 3-level Inverse discrete wavelet transform is applied to the watermarked image

coefficient to generate the final secure watermarked image.

4.2 WATERMARK EXTRACTION PROCESS:

Fig 4.3 Watermark Extraction Process Using DWT

In this process firstly 3-level DWT is applied to watermarked image and cover

image which decomposed the image in sub bands. After that the watermark is

recovered from the watermarked image by using the formula of the alpha blending.

According to the formula of the alpha blending the recovered image is given by

28

RW = (WMI - k*LL3) /q

Where, RW= Low frequency estimation of Retrieve watermark LL3= Low frequency

estimation of the original image, WMI= Low frequency estimation of watermarked

image. After extraction work, 3-level Inverse discrete wavelet transform is applied to

the watermark image coefficient to generate the final watermark extracted image. For

each video stream, a comparison was performed between two sets of experimental

results: original video stream versus MJPEG video stream and original video data

versus watermarked video stream. The comparisons were quantified using the

standard video quality metric: PSNR, which is a well-known quantitative measure in

multimedia processing used to determine the fidelity of a video frame and the amount

of distortion found in it [15].

4.3 PERFORMANCE METRICES FOR WATERMARKING:

4.3.1 PEAK SIGNAL TO NOISE RATIO (PSNR):

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 [13].

PSNR is most commonly used to measure the quality of reconstruction of lossy

compression codec (e.g., for image compression). The signal in this case is the

original data, and the noise is the error introduced by compression. When comparing

compression codecs, PSNR is an approximation to human perception of

reconstruction quality. Although a higher PSNR generally indicates that the

reconstruction is of higher quality, in some cases it may not. One has to be extremely

careful with the range of validity of this metric; it is only conclusively valid when it is

used to compare results from the same codec (or codec type) and same content. The

PSNR between watermarked image and original image is calculated using following

equations,

PSNR =

(1)

29

Typical values for the PSNR in lossy image and video compression are between 30

and 50 dB, provided the bit depth is 8 bits, where higher is better. For 16-bit data

typical values for the PSNR are between 60 and 80 Db.

4.3.2 MEAN SQUARE ERROR (MSE):

MSE is a risk function, corresponding to the expected value of the squared

error loss or quadratic loss. The difference occurs because of randomness or because

the estimator doesn't account for information that could produce a more accurate

estimate.

MSE =

∑ ∑

(2)

Where 255 is the maximum pixel value in the grey-scale image and MSE is the

average mean-squared error, as defined in (2). Here, f and k are the two compared

images, the size of each being M × N pixels (256 × 256 pixels in our experiment).

4.3.3 STRUCTURAL SIMILARITY INDEX MATRIX (SSIM):

SSIM index is a framework for quality assessment based on the degradation of

structural information. For human visual system a calculation of structural information

difference can provide a good approximation to the image distortion perceived. The

product of the illumination and the reflectance gives the luminance of the surface of

an object. But the structures of the objects in the scene are independent of the

illumination [17]. SSIM index defines the structural information in an image as those

attributes that represent the structure of objects in the scene, independent of the

average luminance and contrast.

SSIM =

(3)

Where and and be the mean of x, the variance of x, and the covariance of x

and y respectively. And can be viewed as estimates of the luminance and

contrast of x, and measures the tendency of x and y to vary together and , and

are small constant given by,

30

( , and Where L is the dynamic range of the

pixel values (L=255 for bits/pixel grayscale images) and <<1 and <<1 are two

constants.

4.3.4 NORMALISED CORRELATION FACTOR (NC):

After extracting and refining the watermark, similarity measurement of the

extracted and the referenced watermarks is used for objective judgment of the

extraction fidelity and it is defined as:

NC = ∑ ∑

∑ ∑

(4)

Which is the cross-correlation normalized by the reference watermark energy to give

unity as the peak correlation. Where W (i, j) is the original watermark, W (i, j)^* is the

extracted watermark. Where, NC is the normalized correlation. NC value is 1 when

the watermark and the extracted watermark are identical and zero if the two are

different from each other.

31

CHAPTER 5

RESULTS AND DISCUSSION

In this project, original color video with 210 frames of size 256*256 and

watermark binary color image of size 128*128 are taken as an input. Then

watermarked video is taken as an output. Watermark bits are embedded in each

frequency component of original color video frames. At last PSNR between the

original video frames and watermarked video frames is calculated to see the

invisibility of watermark. Then NC between the watermark frames and recovered

watermark frames are calculated to see the robustness of the scheme.

We can apply different watermark image for different original video image,

this technique is used when there is a requirement of embedding large amount of data.

Using this technique we can embed large number of information, but it will require

more time for embedding than first scene change based method. Here the NC value is

1 for all images, this shows the 100% recovery of watermark images, the PSNR value

is between 41 to 55 db. So watermark embedding in 4th bit is more advantageous than

5th

bit But it is less secure than experiment 1 and 2.so generally watermark embedding

done in the middle bit (5th bit ) of Frequency coefficient. The PSNR and MSE value

shows that the algorithm keeps the quality of the image and invisibility of embedded

watermark without any attacks.

Fig 5.1 Original Input Video

32

Frame 1 Frame 11 Frame 21

Frame 31 Frame 41 Frame 51

Frame 61 Frame 71 Frames 81

Fig 5.2(a) Original Input Video Frames 1-81

33

Frame 91 Frame 101 Frame 121

Frame 131 Frame 141 Frame 151

Frame 161 Frame 171 Frame 181

Fig 5.2 (b) Original Input Video Frames 91-181

34

Frame 191 Frame 201

Fig 5.2 (c) Original Input Video Frames 191-201

Fig 5.3 Watermark Image

Frame 1 Frame 11 Frame 21

Fig 5.3 (a) Watermarked Video Frames 1-21

35

Frame 31 Frame 41 Frame 51

Frame 61 Frame 71 Frame 81

Frame 91 Frame 101 Frame 111

Fig 5.3 (b) Watermarked Video Frame 31-111

36

Frame 121 Frame 131 Frame 141

Frame 151 Frame 161 Frame 171

Frame 181 Frame 191 Frame 201

Fig 5.3 (c) Watermarked Video Frames 121-201

37

Fig 5.4 Extracted Watermark Image

Table 5.1 COMPARISONS OF PSNR, SSIM, MSE and DIFFERENT

WATERMARKED FRAMES:

Frame

No

Discrete Cosine Transform Discrete Wavelet Transform

PSNR(dB) MSE SSIM(dB) PSNR (dB) MSE SSIM (dB)

Frame 1 42.3163 2.35 0.9624 54.2436 0.0044 1

Frame 11 42.1613 2.35 0.9619 54.2538 0.0044 1

Frame 21 42.1619 2.35 0.9621 54.2484 0.0044 1

Frame 31 42.1929 2.35 0.9615 54.2446 0.0044 1

Frame 41 42.1709 2.35 0.9619 54.2521 0.0044 1

Frame 51 42.1728 2.35 0.9619 54.2497 0.0044 1

Frame 61 42.1820 2.35 0.9619 54.2523 0.0044 1

Frame 71 42.1516 2.35 0.9624 54.2919 0.0044 1

Frame 81 42.2331 2.35 0.9625 54.3040 0.0044 1

Frame 91 42.1673 2.35 0.9608 54.3753 0.0044 1

Frame 101 42.3163 2.35 0.9624 54.2221 0.0044 1

Fram111 42.3163 2.35 0.9624 54.2150 0.0044 1

Frame 121 42.1631 2.35 0.9651 54.2147 0.0044 1

Frame 131 42.1719 2.35 0.9644 54.2467 0.0044 1

Frame 141 42.2529 2.35 0.9627 54.2916 0.0044 1

38

Frame No Discrete Cosine Transform Discrete Wavelet Transform

PSNR(dB) MSE SSIM(dB) PSNR (dB) MSE SSIM (dB)

Frame 151 42.2410 2.32 0.9643 54.2508 0.0044 1

Frame 161 42.2574 2.35 0.9610 54.2424 0.0044 1

Frame 171 42.2403 2.33 0.9615 54.2516 0.0044 1

Frame 181 42.2372 2.31 0.9620 54.2437 0.0044 1

Frame 191 42.2145 2.35 0.9642 54.2372 0.0044 1

Frame 201 42.2838 2.35 0.9640 54.1932 0.0044 1

Fig 5.5 (a). PSNR comparison between DCT& DWT

0

10

20

30

40

50

60

Fram

e 1

Fram

e 1

1

Fram

e 2

1

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e 3

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01

P

S

N

R

v

a

l

u(

d

B)

No of Frames

DCT PSNR(dB)

DWT PSNR (dB)

39

Fig 5.5 (b) Normalised Correlation Factor Comparison between DCT& DWT

Fig 5.5 (c) SSIM Comparison between DCT & DWT

0

0.2

0.4

0.6

0.8

1

1.2

Fram

e 1

Fram

e 1

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01

NC

val

ue

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DCT NC

DWT NC

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

Fram

e 1

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e 1

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91

Fram

e 2

01

SS

IM V

alu

e (d

B)

No of frames

DCT SSIM(dB)

DWT SSIM (dB)

40

Fig 5.5 (d) MSE Comparison between DCT & DWT

0

0.5

1

1.5

2

2.5

Fram

e 1

Fram

e 1

1

Fram

e 2

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Fram

e 2

01

MS

E V

alu

e

No of frames

DCT

DWT

41

CHAPTER 6

CONCLUSION

Video watermarking is a secure technique for legal distribution of data. This

project describes the embedding and extraction of watermark into video and

watermarking process. DWT helps in preventing the video from frame drooping,

adding noise/another video. In this, a video watermarking scheme based on wavelet

decomposition has been presented. Watermark is embedded in the randomly selected

frames. Watermark is embedded in mid frequency component to make it robust

against the low frequency attack. The PSNR, MSE, SSIM and Normalization

coefficients are computed for the validity of the proposed method. From the computed

value it is evident that this scheme is able to embed the watermark without any

appreciable degrading in the video. The quality of the extracted watermark is also

same as that of the original one. Apart from this, because of using two key, the

security of the scheme is also doubled.

As future enhancement, we can make the Simulink blocks for this proposed

design and mapping of HDL to field programmable gate array boards can be done.

This will prove the effectiveness of the proposed algorithm at higher level hardware

implementations.

42

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Pp. 133-137.

[3] G Prbhakaran, R.Bhavani, M.Ramesh, “A Robust QR- Code Video Watermarking

Scheme Based on SVD and DWT. Composite Domain” International Conference On

Pattern Recognition, Informatics And Mobile Engineering (PRIME) .IEEE 2013.

[4] Yujie Zhang (2012), “Research On Video Copyright Protection System,” IEEE,

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44

CHAPTER 8

LIST OF PUBLICATIONS

PUBLICATION IN INTERNATIONAL CONFERENCE

[1] Nithya.T, Kirthika.A, “DWT BASED WATER MARKING SYSTEM FOR

VIDEO AUTHENTICATION USING REGION OF INTEREST”, IEEE

Sponsored 3rd

International Conference on innovations in information,

Embedded and communication system (ICIIES’16), Karpagam College of

technology, held on 17th

&18th

march 2016.

[2] Nithya.T, Kirthika.A, “DWT BASED WATER MARKING SYSTEM FOR

VIDEO AUTHENTICATION USING REGION OF INTEREST”, in Middle

East Journal of Scientific Research (Annexure-II) 2016.