Seminar Report on Image Watermarking

Seminar Report on Multipurpose Image Watermarking

--------------------------------------------------------------------------------------------------------------------------------------

Chapter - 1

INTRODUCTION

Digital multimedia work including music, photographs, and movies, have proliferated

during the last decade with the advent of the World Wide Web, e-commerce, and broadband

connectivity to the home. This technical and economic revolution has ushered in a brave new

world for content creators, publishers, distributors, and consumers. The advantages of digital

media are many, including efficient storage and manipulation, fast and inexpensive

distribution, and the ability to customize the media to each unique user. However, these

advantages are tempered by serious concerns regarding illegal copying, the viability of new

e-commerce business models, and the uncertainty surrounding newly enacted copyright laws.

Digital rights management concerns itself with all these issues. While rapid advances

in technology have created the current situation (where illegal digital copies can be created at

virtually no cost and transmitted almost instantly to anyone in the world), technology alone is

unlikely to provide a comprehensive solution. Clearly, business models, licensing

agreements, and new case law will have very significant roles to play.

The ease of copying digital information without any loss of quality violates the

conservation of mass property of traditional media, which inhibited wide global distribution

in the past. On the Internet today it is possible to duplicate digital information a million-fold

and distribute it over the entire world in seconds. These issues worry creators of intellectual

property to the point that they do not even consider to publish on the Internet. Copy

protection, copyright protection, and content authentication have, therefore, been the three

most important issues in the digital world. Conventional cryptographic systems permit only

valid key holders access to encrypted data, but once such data is decrypted, there is no way to

track its reproduction or retransmission [1].

To solve the problem of publishing digital images, researchers have come up with

digital image watermarking [1]. This method allows the owner of an original image to add an

invisible watermark to the digital image before publishing it. The watermark serves to claim

copyright on the image. The owner protects the watermark with a cryptographic secret key,

preventing anybody not possessing the secret key from reading or even detecting the

watermark. The watermark is also supposed to be robust against image tampering. Therefore

anybody who wants to distribute the image further will also distribute the watermark with it,

--------------------------------------------------------------------------------------------------------------------------------

1


--------------------------------------------------------------------------------------------------------------------------------------

violating the copyright on the image. If the copyright holder can detect the fraud, he can

prove ownership by showing that the image contains his proper private watermark.

This scheme works well in a “nice and tidy" clean-room research environment. But in

the “real world" people play by different rules. The systems are not used as intended in the

research environment and hidden back doors are exploited. This report shows the problems

that current digital image watermarking schemes face.

1.1. “Real World" Concerns

Unfortunately the “real world" differs significantly from research labs. Computer

programmers or researchers assume that the end users will behave and think as “logically" as

they do. For example image watermarking is used by a wide variety of users and should still

be equally applicable and efficient for everybody. Unfortunately this is not the case for

today's systems.

1.2. User Interface Concerns

In the “real world" the users have only a limited understanding of the underlying

mechanisms of image watermarking. They do not want to spend hours of training to use one

function of their image processing software. Watermarking should be like a “black box"

where the user enters his original image and by some magic the box outputs the watermarked

image. No specific user understanding should be necessary.

Usually artists are the designers of images. The observation that artists do not like to

degrade their work deliberately by inserting a watermark leads us to believe that they will

either insert a weak watermark or not insert anything at all. Security is not always a strong

enough argument to convince artists to lower their image quality.

Nowadays the user interface (UI) becomes very important: it must present the user

with a clear model of the effects of watermarking and protect him from misuse. It needs to

iron out users misconceptions about the watermarking technique. This observation is equally

true for other security software. Whitten showed in [26] that the security of the system should

not rely on user understanding but be an implicit property of the software. Therefore the UI

becomes a crucial point in any security system.

1.3. Legal Issues

--------------------------------------------------------------------------------------------------------------------------------

2


--------------------------------------------------------------------------------------------------------------------------------------

The real world challenges in image copyrighting are as follows. The scenario is the

following: in a country that does not adhere to the Berne convention [27] on copyright

protection, a malicious person sets up his web-server distributing copyrighted images, music,

etc. There is no way to prevent this person from his “illegal" distribution, as the country does

not provide the legal basis for prosecution. All forms of intellectual property share this

common problem. The past has shown that such scenarios are not far fetched. In fact recent

incidents prove that this attack is quite common. The situation gets worse as the Internet

expands its range with high-speed connections to countries that are “traditionally" known for

copyright infringement. If these countries do not change their laws, this problem cannot be

solved trivially by technical means.

1.4. Web Spider Issues

Web-spiders which scan the web for stolen images face the same problem as stated

above: the web-server detects that the request originates from the spider and it will then not

forward any illegal images to that site, or replace them with dummy images. Access

controlled or pay-sites present another problem to the web-spider. It cannot access the

contents without paying or authenticating itself. Unfortunately we believe that the largest part

of copyright infringement comes from access-controlled sites. These problems present high

barriers for any web-spider to overcome.

Various schemes have been proposed to do rights management [13,25]. Rights

management is where the information distributor can give usage access rights to the client.

For example he could declare that only viewing, not printing of the image is possible. This

sounds feasible at first, but again, things look differently in the real world. Even if using a

tamper-proof smartcard in everybody’s PC that checks the access rights prior to any action

enforces the rights, the image can be stolen anyway. While the image is displayed on the

screen, the information has to be present somewhere in the PC's memory. Therefore that

memory can also be read by another program and saved. Watermarking faces a similar

problem: the image already has to be present somewhere in memory before the watermark

can be extracted. Similar concerns are discussed with respect to DVD rights management in

[37].

--------------------------------------------------------------------------------------------------------------------------------

3


--------------------------------------------------------------------------------------------------------------------------------------

Another reason things look differently in the real world is that the systems are used in

ways not foreseen by the system designers. In watermarking we could imagine the following

problem: Instead of showing the stolen image at once on a web-page, Mallory chops up the

image into small blocks and creates many small images. In the web page, the images are then

arranged such that the viewer can see the original image again. Unfortunately, the individual

images are too small to carry an extractable watermark. Only if multiple blocks were merged,

the watermark could be extracted again. Therefore a web-spider could not detect the fraud,

since it checks each image individually. This mosaic attack was also discovered

simultaneously by Anderson and Petitcolas.

1.5. Hacker Circles

If Mallory wanted to remove a watermark and she knows about ways of using her

image processing toolkit with which she could potentially remove the watermark she would

probably hesitate to do so. First, she knows that there is a large fine for copyright

infringement and she is unsure if the attack would be successful. Second she is quite unsure

of which transformations to apply - she did tests on her own watermarks, but are the

commercial marks the same?

Hackers are globally well organized and already today there exist multiple programs

to remove watermarks. Therefore Mallory does not need to be a signal or image processing

expert to remove the copyright with a high certainty. The power of such underground activity

to create high quality watermark removal software should not be underestimated.

--------------------------------------------------------------------------------------------------------------------------------

4


--------------------------------------------------------------------------------------------------------------------------------------

Chapter – 2

WATERMARKING

2.1 INTRODUCTION

The process of embedding information into another object/signal can be termed as

watermarking. The watermark is one, which is imperceptibly added to the cover-signal in

order to convey the hidden data. The basic idea of watermarking is to embed a stealthy image

(unperceivable image) into the image needed to be secured. The stealthy image is derived

from the original image and should carry sufficient information to ensure copyright

verification. It should be imperceptible, undetectable, and robust enough to survive any

attempts to destroy it.

Cryptography has been the cornerstone [38] of technologies used to protect

intellectual property rights. For example, cable and satellite television signals are commonly

scrambled to prevent unauthorized viewing. However, cryptography only protects the work

during transmission or distribution. It provides no protection after the work is decrypted. And

all work must eventually be decrypted if consumers are to enjoy the photograph, music, or

movie. Watermarking is a technology that complements cryptography by embedding

imperceptible signals in a work. These signals remain in the work after decryption and even

after conversion to the analog world, and their use has been proposed for a variety of digital

rights management purposes.

Digital watermarking is only possible because our vision system is not perfect. A

perfect vision system can be defined as one that has the capability of distinguishing even the

slightest changes in visual stimuli. For the human visual system this is, however, not the case.

A digital watermark is best described by comparing it to a traditional paper watermark.

Traditional watermarks are added to some types of paper to offer proof of authenticity. They

are imperceptible, except when the paper is held up to a light for inspection. Similarly, digital

watermark is added to a digital image in a way that can be seen by a computer but is

imperceptible to the human eye. A digital watermark carries a message containing

information about the creator or distributor of the image, or even about the image itself.

--------------------------------------------------------------------------------------------------------------------------------

5


--------------------------------------------------------------------------------------------------------------------------------------

A digital watermark is used to communicate copyright information about an image in

order to reduce copyright infringement. A person opening a digitally watermarked image in

an image-editing application, the reader receives notification through a copyright symbol

((c)) that the image contains copyright and ownership information. The digital watermark can

provide a link to complete contact details for the copyright holder or image distributor,

making it easy for the viewer to license the image, license another one like it, or commission

new work. Clearly watermarking is a form of communications, and a number of models have

been proposed. A watermarking system consists of a watermark embedder and a watermark

detector, which are analogous to a transmitter and receiver. The most common model then

views the original work as the communication channel and the watermark as the modulated

signal that conveys the message. However, unlike traditional communications, watermarking

must also pay careful attention to the fidelity of the underlying cover work. This has

sometimes been modeled as a power constraint at the embedder (transmitter). A more recent

model considers watermarking as a form of communications with side information. In this

model, the cover work is no longer treated as noise but as side information available to either

the transmitter and/or receiver. This communications perspective holds the promise of greatly

improving the channel capacity of a watermarking system.

Unlike printed watermarks, which are intended to be somewhat visible, digital

watermarks are designed to be completely invisible, or in the case of audio clips, inaudible.

Moreover, the actual bits representing the watermark must be scattered throughout the file in

such a way that they cannot be identified and manipulated. And finally, the digital watermark

must be robust enough so that it can withstand normal changes to the file, such as reductions

from lossy compression algorithms.

Ideal properties of a digital watermark have been stated in many articles and papers.

These properties include:

1) A digital watermark should be perceptually invisible to prevent obstruction of the original

image.

2) A digital watermark should be statistically invisible so it cannot be detected or erased.

3) Watermark extraction should be fairly simple. Otherwise, the detection process requires

too much time or computation.

--------------------------------------------------------------------------------------------------------------------------------

6


--------------------------------------------------------------------------------------------------------------------------------------

4) Watermark detection should be accurate. False positives, the detection of a nonmarked

image, and false negatives, the non-detection of a marked image, should be few.

5) Numerous watermarks can be produced. Otherwise, only a limited number of images may

be marked.

6) Watermarks should be robust to filtering, additive noise, compression, and other forms of

image manipulation.

7) The watermark should be able to determine the true owner of the image.

2.1.1 REASONS TO DIGITALLY WATERMARK IMAGES

There are several reasons for using digital watermarks. One is simple pride of

authorship--the same reason that artists sign their paintings. Digital images are especially

prone to loss of authorship, as seen by the avalanche of images posted daily on the World

Wide Web, few of which have any reference to the photographer or illustrator.

Another reason is, more important, commercially: a digital watermark communicates

the name and rights of an image's copyright holder. With this information, the image

consumer can quickly and easily contact the image creator or distributor to license the work

or commission additional work.

Overall, digital watermarking provides creators and distributors of images three main

benefits:

• Protect your valuable images by communicating your copyright.

• Track down uses of your images on the Web.

• Generate incremental revenue by embedding an ad in every image.

In general, there are two types of digital watermarks addressed in the existing

literature: visible and invisible watermarks. A visible watermark typically contains a visible

message or a company logo indicating the ownership of the image. On the other hand, the

invisibly watermarked digital content appears visually very similar to the original. Examples

include images distributed over Internet with watermarks embedded in them for copyright

protection. Those which fail can be classified as visible watermarks. Examples include logos

used in papers in currencies.

--------------------------------------------------------------------------------------------------------------------------------

7


--------------------------------------------------------------------------------------------------------------------------------------

2.2. TYPES OF WATERMARK :

Invisible watermarks can be broadly classified into two types, robust and fragile (or

semi-fragile) watermarks.

2.2.1. Robust Watermark

A robust watermark [2]-[7] should be stuck to the document it has been embedded in,

in such a way that any signal transform of reasonable strength cannot remove the watermark.

Hence a pirate willing to remove the watermark will not succeed unless they debase the

document too much to be of commercial interest. This form of watermark is the very

challenging and attracts most research.

For any watermark to be robust, the watermark information must be embedded in the

target medium in such a way that removing this information irreparably damages the

medium.

When considering static images, the commonly recognized transformations that a

watermark should survive are:

Rotation, scaling, translation, mirroring.

Filtering (Gaussian blur, image sharpening, etc.)

Adding noise to the image, adding jitter (duplicating and removing lines/columns of

the image), cropping (removing the sides).

Color remapping (color, quantization, and adjustments in brightness or contrast).

Lossy compression (JPEG, MPEG, fractal compression).

All of these transformations can preserve the value of the image to the user. After they have

been applied, the image is still recognizably derived from the original image. This concept

can be used as the foundation of a robust watermarking method [29]; if the image can be

transformed into some space which is invariant to the value preserving transforms listed

above, the watermark can be applied in that space before reversing the transformation to

arrive back at the original image. This watermark will then also be invariant under these

transformations.

2.2.2. Semi-fragile Watermark

A (semi-) fragile watermark [8]-[11] is a mark, which is (highly) sensitive to a

modification of the stego-medium. A fragile watermarking scheme should be able to detect

--------------------------------------------------------------------------------------------------------------------------------

8


--------------------------------------------------------------------------------------------------------------------------------------

any change in the signal and identify where it has taken place and possibly what the signal

was before modification. It serves at proving the authenticity of a document.

2.3 PROPERTIES OF WATERMARKING TECHNIQUES

2.3.1. Unobtrusive (Difficult to notice)

The watermark should be perceptually invisible to the viewer nor should the watermark

degrade the quality of the content. In earlier work [33,34,35,36],Cox , had used the term

“imperceptible", and this is certainly the ideal. However, if a signal is truly imperceptible,

then perceptually based lossy compression algorithms should, in principle, remove such a

signal. Current state-of-the-art compression algorithms probably still leave room for an

imperceptible signal to be inserted. This may not be true of next generation compression

algorithms. Thus, to survive the next generation of lossy compression algorithms, it will

probably be necessary for a watermark to be noticeable to a trained observer.

2.3.2. Robustness

The watermark must be difficult (hopefully impossible) to remove. Of course, in theory, any

watermark may be removed with sufficient knowledge of the process of insertion. However,

if only partial knowledge is available, for example, the exact location of the watermark within

an image is unknown, then attempts to remove or destroy a watermark by say, adding noise,

should result in severe degradation in data fidelity before the watermark is lost. In particular,

the watermark should be robust to the following attacks and characteristics.

i. Universality: The same digital watermark algorithm should apply to all three media

types. This is potentially helpful in the watermarking of multimedia products. Also, this

feature is conducive to implementation of audio, image, and video watermarking algorithms

on common hardware.

ii. Tamper-resistance: The watermarking techniques should be robust to legitimate

signal distortions as well as intentional attacks to remove or tamper with the digital

watermark.

--------------------------------------------------------------------------------------------------------------------------------

9


--------------------------------------------------------------------------------------------------------------------------------------

iii. Common Signal Processing: The watermark should still be retrievable even if

common signal processing operations are applied to the data. These include, digital-to-analog

and analog-to-digital conversion, resampling, requantization (including dithering and

recompression), and common signal enhancements to image contrast and color, or audio bass

and treble, for example.

iv. Common Geometric Distortions: Watermarks in image and video data should also

be immune from geometric image operations such as rotation, translation, cropping, and

scaling.

v. Subterfuge Attacks: In addition, the watermark should be robust to collusion by

multiple individuals who each possess a watermarked copy of the data. That is, the

watermark should be robust to combining copies of the same data set to destroy the

watermarks. Further, if a digital watermark is to be used as evidence in a court of law, it must

not be possible for colluders to combine their images to generate a different valid watermark

with the intention of framing a third-party.

2.3.3. Secure

The watermarked image should not reveal any clues of the presence of the watermark,

with respect to unauthorized detection, or (statistical) indefectibility or unsuspicious (not the

same as imperceptibility).

--------------------------------------------------------------------------------------------------------------------------------

10


--------------------------------------------------------------------------------------------------------------------------------------

Chapter - 3

TECHNICAL PROBLEMS OF WATERMARKING

3.1. THE WATERMARKING PROCESS


watermarking.

The technical challenge that watermarking presents is the successful concealment of

the watermark signal in a much larger bandwidth medium. As [28] points out, usually an

attacker's ability to read or, worse, change or remove the watermark, is of more concern than

their ability to detect its presence. In the face of perfect compression methods, it is not clear

that it is possible to conceal the presence of a watermark.

An often-used method for covert channel communication over a higher-bandwidth

medium is spread spectrum communications. This method can also be applied to

watermarking images, as described in [30,31]. It has the advantage of having a long history in

both military and civilian digital communications, and is thus both well understood and of

--------------------------------------------------------------------------------------------------------------------------------

11


--------------------------------------------------------------------------------------------------------------------------------------

proven reliability. At the current time, it appears the most promising framework for

embedding watermarks in either images or other digital media data streams.

Watermarks are most often used for copyright control. It is commonly recognized that

no watermarking method can withstand all possible attacks. Thus, in the domain of network-

accessible digital media, it may be better to regard a watermark as an aid to quickly

comparing possible copies of a copyrighted work to the original, in order to locate copyright

violations, than as an indicator of copyright in itself.

3.2. ATTACKS ON WATERMARKS

The possible attacks against watermarks are wide and varied. Cox and Linnartz [37]

and Anderson [14] present a number of such attacks, including:

Image modification attacks. These use image transformations such as those listed

above.

Bit-level attacks. If the attacker has access to a watermark presence detector, the

contents and location of the watermark can be derived. This also makes it much easier

to remove a watermark.

Watermark-insertion attacks. If the attacker has access to a watermark insertion

device, and the watermarking process is not a one-way function, it is possible to

recover the original, unwatermarked image, by pre-distorting the copy, and

rewatermarking it.

Statistical averaging attacks. The attacker uses multiple watermarked images to

estimate the watermark, and then subtracts this from the image. This is especially a

problem with video since a large number of watermarked frames is available.

Scrambling attacks. By inserting a scrambler before the watermark detector, and a de-

scrambler after it, detection of the watermarking can be avoided.

As it can be seen from this list, even with a perfect watermarking method, there are

various system-level attacks that can frustrate the secure use of watermarks in a copyright

scheme.

3.3. PRIORITIZING ATTACK RESISTANCE

As making a watermark resistant to a large number of image transformation attacks is

a difficult task, it is important to prioritize these transforms. A watermark should aim to be

most resistant to the most common attacks; as we have pointed out, complete watermark

--------------------------------------------------------------------------------------------------------------------------------

12


--------------------------------------------------------------------------------------------------------------------------------------

security is an unachievable goal. It is more realistic to aim for a high probability of recovery.

A corollary of this is that a watermarking method must first of all be robust against those

transformations used by legal users of the image. It is tempting to concentrate on the various

complex attacks that malicious attackers might employ. However, this is pointless if the

method is not properly resistant to image edits made by either the original owner or valid

users of the image. Roughly in order of merit, a watermarking method must handle:

Scaling, especially (filtered) down-sampling.

GIF and JPEG compression. (Color quantization, and lossy compression.)

Simple brightness, contrast, or gamma adjustment.

Border cropping.

3.4. HANDLING SMALL IMAGES

Below a certain size it becomes difficult to embed and reliably recover a watermark

from an image. Even the better watermarking methods have difficulty retaining watermarks

in 100 by 100 pixel images that are lossily compressed. It is not clear that this problem can

ever be solved by technical means, as it is stems from a simple lack of bandwidth. As the

effective bandwidth of the picture is (roughly) proportional to its area, halving the dimensions

of it reduces the available bandwidth by 4. Often small images are also color-reduced for

efficient storage, further restricting the available bandwidth. This observation provides the

foundation of the mosaic attack described in section 2.

It should be noted that as image size drops, the concern about copyright violations

also drops. Small text extracts from larger works are allowed for personal use under the

copyright act [32]. In music, samples of a couple of seconds or less, especially if they have

been distorted in some way, are generally accepted as “fair use". Longer, more recognizable

samples require copyright clearance.

Most of the literature on watermarks avoids mentioning this problem, because it

seems obvious. However end users of watermarking are often not aware of the problems

small images can cause, or if they are, are uncertain as to how large an image has to be before

its watermark is secure. This makes it essential that a watermarking system has some

mechanism for warning a user that a small-image watermark may not be robust.

In order to fulfill multipurpose applications, several multipurpose watermarking

algorithms based on wavelet transform [11] and fast Fourier transform [12] have been

--------------------------------------------------------------------------------------------------------------------------------

13


--------------------------------------------------------------------------------------------------------------------------------------

presented. Traditional digital watermarking schemes are mainly based on DCT and DWT

transforms. Recently, some robust image watermarking techniques based on vector

quantization (VQ) [15]–[21] have been presented. [15]–[18] embed the watermark

information into the encoded indices under the constraint that the extra distortion is less than

a given threshold. [19] embeds the watermark bit in the dimension information of the variable

dimension reconstruction blocks of the input image. [20], [21] embed the watermark

information by utilizing the properties, such as mean and variance, of neighboring indices.

Multipurpose watermarking method based on multistage vector quantization will be

discussed.

Chapter – 4

VECTOR QUANTIZATION

4.1. INTRODUCTION

Vector quantization (VQ) has become an attractive blockbased encoding method for

image compression in recent years. It can achieve a high compression ratio. In environments

such as image archival and one-to-many communications, the simplicity of the decoder

makes VQ very efficient. In brief, VQ can be defined as a mapping from k-dimensional

Euclidean space Rk into a finite subset C={ci /i=0,1,…,N-1} that is generally called a

codebook, where ci is a codeword and N is the codebook size. VQ first generates a

representative codebook from a number of training vectors using, for example, the well-

known iterative clustering algorithm [22] that is often referred to as the Generalized Lloyd

Algorithm (GLA).

--------------------------------------------------------------------------------------------------------------------------------

14


--------------------------------------------------------------------------------------------------------------------------------------

In VQ, the image to be encoded is first decomposed into vectors and then sequentially

encoded vector by vector. In the encoding phase, each k-dimensional input vector x=(x1,x2,…

xk) is compared with the codewords in the codebook C={c0,c1,…c(N-1)} to find the best

matching codeword ci=(ci1,ci2,…,cik) satisfying the following condition:

d(x,ci) = min d(x,cj) (1)

0≤j≤N-1

i.e., the distance between x and cj is the smallest, where d(x,cj) is the distortion of

representing the input vector by the codeword cj, which is often measured by the squared

Euclidean distance, i.e.

2 (2)

Then, the index of the best matching codeword assigned to the input vector x is

transmitted over the channel to the decoder. The decoder has the same codebook as the

encoder. In the decoding phase, for each index , the decoder merely performs a simple table

lookup operation to obtain ci and then uses ci to reconstruct the input vector . Compression is

achieved by transmitting or storing the index of a codeword rather than the codeword itself.

The compression ratio is determined by the codebook size and the dimension of the input

vectors, and the overall distortion is dependent on the codebook size and the selection of

codewords.

In 1980 Linde, Buzo and Gray proposed an improvement of the Lloyd’s technique.

They extended Lloyd’s results from mono- to k-dimensional cases. For this reason their

algorithm is known as the Generalized Lloyd Algorithm (GLA) or LBG [23] from the initials

of its authors.

This algorithm is similar to the k-means algorithm.

4.2 THE ALGORITHM:

1. Determine the number of codewords, N, or the size of the codebook.

2. Select N codewords at random, and let that be the initial codebook. The initial

codewords can be randomly chosen from the set of input vectors.

--------------------------------------------------------------------------------------------------------------------------------

15


--------------------------------------------------------------------------------------------------------------------------------------

3. Using the Euclidean distance measure clusterize the vectors around each codeword.

This is done by taking each input vector and finding the Euclidean distance between it

and each codeword. The input vector belongs to the cluster of the codeword that

yields the minimum distance.

4. Compute the new set of codewords. This is done by obtaining the average of each

cluster. Add the component of each vector and divide by the number of vectors in the

cluster.

where i is the component of each vector (x, y, z, ... directions), m is the number of

vectors in the cluster.

1. Repeat steps 2 and 3 until the either the codewords don't change or the change in the

codewords is small.

This algorithm is by far the most popular, and that is due to its simplicity. Although it is

locally optimal, yet it is very slow. The reason it is slow is because for each iteration,

determining each cluster requires that each input vector be compared with all the

codewords in the codebook.

4.3 WORKING OF THE SEARCH ENGINE

Although VQ offers more compression for the same distortion rate as scalar

quantization, yet is not as widely implemented. This is due to two things. The first is the

time it takes to generate the codebook, and second is the speed of the search. Many

algorithms have been proposed to increase the speed of the search. Some of them reduce the

math used to determine the codeword that offers the minimum distortion; other algorithms

preprocess the codewords and exploit underlying structure.

The simplest search method, which is also the slowest, is full search. In full search an

input vector is compared with every codeword in the codebook. If there were M input

--------------------------------------------------------------------------------------------------------------------------------

16


--------------------------------------------------------------------------------------------------------------------------------------

vectors, N codewords, and each vector is in k dimensions, then the number of multiplies

becomes kMN, the number of additions and subtractions become MN((k - 1) + k) = MN(2k-1),

and the number of comparisons becomes MN(k - 1). This makes full search an expensive

method.

4.4 MEASURE OF PERFORMANCE VQ

How does one rate the performance of a compressed image or sound using VQ?

There is no good way to measure the performance of VQ. This is because the distortion that

VQ incurs, will be evaluated by us humans and that is a subjective measure. It can always be

resorted to good old Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR).

MSE is defined as follows:

where M is the number of elements in the signal, or image. For example, if we wanted to find

the MSE between the reconstructed and the original image, then we would take the difference

between the two images pixel-by-pixel, square the results, and average the results.

The PSNR is defined as follows:

where n is the number of bits per symbol. As an example, if we want to find the PSNR

between two 256 gray level images, then we set n to 8 bits.

In the decoding phase, not the original but the watermarked codeword is used to

represent the input image block. Therefore, the VQ-based digital image watermarking will

introduce some extra distortion. Whether the original image is required or not during the

watermark extraction is dependent on the embedding method. In these algorithms, the

codebook is open for users but the partition is the secret key. Experimental results show that

these algorithms are robust to VQ compression with high-performance codebooks, JPEG

compression and some spatial image processing operations. However, these algorithms are

fragile to rotation operations and VQ compression with low-performance codebooks.--------------------------------------------------------------------------------------------------------------------------------

17


--------------------------------------------------------------------------------------------------------------------------------------

4.5 WATERMARKING ALGORITHMS BASED ON INDEX PROPERTIES

To enhance the robustness to rotation operations and VQ compression operations,

some image watermarking algorithms [21], [22] based on the properties of neighboring

indices have been proposed. In [21], the original watermark W with size Aw * Bw is first

permuted by a predetermined key,key1 , to generate the permuted watermark Wp for

embedding. The original image X with size A * B is then divided into vectors x(m,n) with

size (A/Aw) * (B/Bw), where x(m,n) denotes the image block at the position of (m,n). After

that, each vector x(m,n) finds its best codeword C i in the codebook C and the index is

assigned to x(m,n), we can then obtain the indices matrix Y with elements y(m,n), which can

be represented by

= (3)

For natural images, the VQ indices among neighboring blocks tend to be very similar, so we

can make use of this property to generate the polarities . After calculating the variances of

y(m,n) and the indices of its surrounding blocks with

(4)

We can obtain the polarities as follows:

(5)

where

(6)

--------------------------------------------------------------------------------------------------------------------------------

18


--------------------------------------------------------------------------------------------------------------------------------------

For convenience, the threshold T is set to be half of the codebook size, N/2.Then the final

embedded watermark or the secret key, key2 , can be generated with the exclusive-or

operation as follows:

key2 = Wp P (7)

After the inverse-VQ operation, both the reconstructed image X’ and the secret key key2 work

together to protect the ownership of the original image. In the extraction process, we first

calculate the estimated polarities P’ from X’ and then obtain an estimate of the permuted

watermark, as follows:

W’p =key2 P’ (8)

Finally, the inverse permutation operation with key1 can be performed to obtain the extracted

watermark W’. In order to embed multiple watermarks, [22] also uses the mean of indices to

generate another polarities P1 for embedding. Experimental results show that these algorithms

are robust to many kinds of attacks, including JPEG, VQ, filtering, blurring, and rotation.

However, these algorithms have the following problem.

1) The codebook should be used as a key, because if the user possesses the same codebook,

he can also embed his own watermark in the watermarked image without any modification.

In fact, unlike traditional watermarking methods, these kinds of watermarking

algorithms do not modify the VQ compressed cover work at all. The term “fingerprint” or

“secure fingerprint” may be more appropriate, and sometimes we can call this kind of

watermark “zero-watermark.” In view of unification, we use the term “robust watermark”

instead of “secure fingerprint”.

--------------------------------------------------------------------------------------------------------------------------------

19


--------------------------------------------------------------------------------------------------------------------------------------

Chapter - 5

MULTISTAGE VECTOR QUANTIZATION

The basic idea of multistage VQ is to divide the encoding task into successive stages,

where the first stage performs a relatively crude quantization of the input vector using a small

codebook. Then, a second-stage quantizer operates on the error vector between the original

and quantized first-stage output. The quantized error vector then provides a second

approximation to the original input vector thereby leading to a refined or more accurate

representation of the input. A third-stage quantizer may then be used to quantize the second-

stage error to provide a further refinement and so on.

In this paper, we adopt a two-stage vector quantizer as illustrated in Fig. 1.

--------------------------------------------------------------------------------------------------------------------------------

20


--------------------------------------------------------------------------------------------------------------------------------------

FIG 1. Two-stage VQ

It is the simplest case and can be used to generate the general multistage vector quantizer.

The input vector x is quantized by the initial or first-stage vector quantizer denoted by VQ1

whose codebook is C1={c10,c11,…,c1(N1-1)} with size N1 . The quantized approximation is

then subtracted from x producing the error vector e2 . This error vector is then applied to a

second vector quantizer VQ2 whose codebook is C2={c20,c21,…,c2(N2-1)} with size N2 yielding

the quantized output .The overall approximation to the input x is formed by summing the

first and second approximations, and . The encoder for this VQ simply transmits a pair

of indices specifying the selected codewords for each stage and the task of the decoder is to

perform two table lookups to generate and then sum the two codewords. In fact, the overall

codeword or index is the concatenation of codewords or indices chosen from each of two

codebooks. That is to say, this is a product code where the composition function of the

decoder is simply a summation of the reproductions from the different two VQ decoders.

Thus, the equivalent product codebook C can be generated from the Cartesian product C1 *

C2. Compared to the full-search VQ with the product codebook C, the two-stage VQ can

reduce the complexity from N=N1 * N2 to N1 + N2.

5.1. EMBEDDING PROCESS

Before describing the proposed algorithm, we make some assumptions. Let X be the

original image with size A * B, let WR and WF be the binary robust and semi-fragile

watermarks with size Aw * Bw respectively. Here, a small visually meaningful binary image V

with size a * b is replicated periodically to obtain the binary semi-fragile watermark WF with

size Aw * Bw that is large enough for embedding.

--------------------------------------------------------------------------------------------------------------------------------

21

VQ1

VQ2+ +

_

•

• +


--------------------------------------------------------------------------------------------------------------------------------------

In each stage of the proposed algorithm, only one bit is embedded in each input image

block (or vector), so the dimension of each input vector or codeword is k=(A/Aw) * (B/Bw).

Assume that the first-stage codebook is C1={c10,c11,…,c1(N1-1)} with size N1 = 2n1 and the

second-stage codebook is C2={c20,c21,…,c2(N2-1)} with size N2 =2n2 where n1 and n2 are natural

numbers. Thus, a binary number with n1 + n2 bits, in which the first n1 bits stand for the index

of Stage 1 and the last n2 bits denote the index of Stage 2, can represent the overall index. The

overall codeword can be selected from the equivalent product codebook C={c0,c1,…c(N-1)}

with size N = N1 * N2. In other words, if the index in codebook C1 is i and the index in

codebook C2 is j, then the equivalent overall index in the product codebook C is i + j * N2 .

In our algorithm, the robust watermark WR and the semi-fragile watermark WF are

embedded in two stages, respectively. We embed the robust watermark in the first stage and

the semi-fragile one in the second stage to enhance the robustness and transparency of the

proposed algorithm. In what follows, we describe the two-stage embedding process

separately.

5.1.1. Robust Watermark Embedding Process

In the proposed algorithm, we adopt the method [20] based on index properties to

embed the robust watermark in the first stage as shown in Fig. 2.

--------------------------------------------------------------------------------------------------------------------------------

22

Division

PermutationEmbeddingIndex Polarities

Computation

Normal VQDecoder

Nearest Neighbor VQ Encoder

Input Vectors

•Codebook C1

+

_

•

Encoded Indices

X

X1

key2WR


--------------------------------------------------------------------------------------------------------------------------------------

FIG. 2 Robust Watermark embedding process in the first stage

The original watermark WR is first permuted by a predetermined key, key1, to generate

the permuted watermark WRP for embedding. The polarities P is then calculated. Finally, we

generate the final embedded watermark or the secret key, key2 , with the exclusive-or

operation. After the first-stage embedding, we can obtain the reconstructed image X’ and the

error image X1 as follows:

X’=VQ1-1[VQ1[X]]

X1 = X – X’

This method has two problems and can be automatically solved, which will be discussed later

in the extraction process.

5.1.1.2. The Embedding Algorithm

Let the input image be X with size M x N. The robust watermark with VQ is to be

embedded into X and a watermarked reconstruction and the secret keys associated with the

--------------------------------------------------------------------------------------------------------------------------------

23

Composition

Output Vectors

•

P

WRP

X’

key1


--------------------------------------------------------------------------------------------------------------------------------------

embedded watermark is to be output. These secret keys are registered to the third party to

certify the ownership of original multimedia source.

The two binary-valued watermarks to be embedded are WR and WF. First the VQ

operation is performed and codewords with the nearest search algorithm are obtained.

Afterwards the watermarks can be embedded with the characteristics of the indices in the VQ

domain. In order to survive the picture cropping attacks, a pseudo-random number traversing

method is applied to permute the watermark to disperse its spatial-domain relationships. With

a predetermined key, key1, in the pseudo-random number generating system, then

WRP =permute(WR,key1)

Then the permuted version WRP, is used for embedding into VQ indices.

In the VQ encoding procedure, X is divided into vectors x, then each x finds its

nearest codeword ck in the codebook C, and the index k is assigned to x for transmission.

During the transmission of the VQ indices, some errors might be induced by the channels,

thus the received indices would not be identical to the transmitted ones. While decoding with

the VQ indices, the decoder merely performs a table look-up process on the received index

k’ to obtain ci’ and the get the reconstructed image X’.

The VQ is performed with the codebook size L. The codebook, C, and the codewords

therein, ck, k [0,L-1], can be represented by

C={c0,c1,…,cL-1}.

The block at the position (m,n) of the original source X is x(m,n). After performing

VQ, the indices Y and y(m,n) can be expressed with

Y=VQ(X),

Y(m,n)=VQ(x(m,n)) C.

To embed the binary watermark into the original source, some relationships to alter

the VQ indices into binary format for further embedding need to be introduced. The

polarities, P, of the VQ indices to embed the watermarks. For natural images, the VQ indices

among the neighboring blocks tend to be very similar, this characteristic can be used to

generate P.

Calculating the mean of y(m,n) and the indices of its surrounding blocks with

--------------------------------------------------------------------------------------------------------------------------------

24


--------------------------------------------------------------------------------------------------------------------------------------

=

Similarly obtaining the variance of y(m,n) and the indices of its neighboring block

with

σ2 (m,n) = - µ2(m,n)

The polarities based on the means , variances and both the means and the variances , can be

decided with the pre-determined threshold value.

The threshold taken = ½ for convenience and L= codebook size. Then the watermark

WRP can be embedded with P by the exclusive-or operation-

key1=WRP P.

5.1.2. Semi-Fragile Watermark Embedding Process

To embed one bit in each index in the second stage, we can adopt an index

constrained vector quantization (ICVQ) encoding scheme as shown in Fig. 3.

--------------------------------------------------------------------------------------------------------------------------------

25

Watermarking Position key3

WF


--------------------------------------------------------------------------------------------------------------------------------------

FIG. 3. Semi-fragile watermark embedding process in the second stage

Because each index has n2 bits, we can select an embedding position from n2

candidate positions. Assume that we select Position key3 , which is considered as a key, to

embed the watermark bit, where 0 ≤ key3 ≤ n2 -1 . Unlike the normal VQ encoder, the

embedding process for each watermark bit can be performed by searching the best match

codeword c2p for each input error vector x1 under the constraints that the key3 th bit of index is

equal to the watermark bit to be embedded. After the normal VQ decoder, we can obtain the

reconstructed error image X1’, as follows:

X1’ = VQ2

-1[ICVQ2[X1]]

Then, we can obtain the final watermarked image , as follows:

XW = X’ + X’1

5.2. EXTRACTION PROCESS

--------------------------------------------------------------------------------------------------------------------------------

26

Division

Composition Normal VQDecoder

Index Constrained VQ Encoder

Codebook C2

Output Vectors

Input Vectors

++ X1

’

X’

X1

XW

Encoded Indices


--------------------------------------------------------------------------------------------------------------------------------------

To enhance the security of the embedding process, the equivalent product codebook C is used

in the extraction process as shown in Fig. 4, that is to say, the two-stage codebooks are used

as secret keys while the product codebook is open for users.

FIG. 4. Watermark Extraction Process

In addition, because the users do not know the codebook sizes used in two-stage VQ

either, how to segment the overall index into two stage indices is also a secret key, key5, to

--------------------------------------------------------------------------------------------------------------------------------

27

Composition

Inverse

Permutation

XOR

Bit judgmentIndex Polarities

Computation

Index Segmentation

Normal VQ EncoderDivision

Inverse Permutation

Codebook Cu

Xw

key4

Segmentation Position key5

Codebook C

Input Vectors

Encoded Indices

Indices of Stage1

Indices of Stage2

Watermarking Position key3

Extracted Bits

Extracted Semi-Fragile

Extracted Robust Watermark WER

Watermark WEF

key2

key1


--------------------------------------------------------------------------------------------------------------------------------------

users. In order to make the embedding algorithm more secretly, we can also permute the

product codebook and then publicize the permuted codebook Cu for users.

The extraction process can be performed without the original image and can be

described as follows. First, perform the inverse permutation operation with key4 on Codebook

Cu to obtain the product codebook C. Second, the watermarked image Xw is divided into

blocks or vectors. Third, the normal VQ encoder performs the nearest neighbor codeword

search on all input vectors to obtain the encoded overall indices. Fourth, according to the two

stage codebook sizes, each overall index is segmented into two indices. One is for robust

watermark extraction; the other is for semi-fragile watermark extraction. Finally, the robust

and semi-fragile watermarks are extracted independently. For the robust watermark

extraction, the polarities P are computed from the indices of Stage 1, and then XOR operation

is performed between P and key2 to obtain the extracted permuted robust watermark WEPR,

and finally perform inverse permutation operation with key1 to obtain the extracted robust

watermark WER . For the semi-fragile watermark extraction, the key3 th bit of each index of

Stage 2 can be checked to obtain the extracted watermark bit, where key3 is just the

watermarking position, and then piece all extracted bits together to form the extracted semi-

fragile watermark WEF. From the above, it can be seen that the advantages of using ICVQ in

the semi-fragile watermarking are as follows:

1) Both the embedding and the extraction processes are very simple.

2) The extraction is blind.

3) The embedded position can be controlled by a key for more security.

In Section C above, the problem of the robust embedding technique [21] are pointed

out. However, in this algorithm, this problem can be automatically solved. Using not the two-

stage codebooks but the equivalent product codebook to extract the watermarks can solve this

problem. From Fig. 4, it can be seen that the extraction time is determined by the codebook

size of C. If N is very large, then the full search VQ encoding is rather a time-consuming

process, so fast codeword search algorithm [24] is used in the proposed algorithm. A new

fast codeword search algorithm for image vector quantization (VQ) is introduced. This

algorithm performs a fast codeword search in the Hadamard Transform (HT) domain using

the partial distance search (PDS) technique. Experimental results show that the algorithm

needs only 2-3% or the distortion calculations of the exhaustive search method

--------------------------------------------------------------------------------------------------------------------------------

28


--------------------------------------------------------------------------------------------------------------------------------------

Chapter – 6

DESIGN

FIG 6.1. The LBG procedure

--------------------------------------------------------------------------------------------------------------------------------

29


--------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------------------------------------------------------

30

Start

Initial codebook (Y0)

M=0;D-1=+∞

New partition calculationSm=P(Ym)

Distortion (Dm) calculation

(Dm-1-Dm) / Dm

< ε

New codebook calculationYm+1=X(Sm’)

ELGB Block

m=m+1

Final codebook (Ym);

End


--------------------------------------------------------------------------------------------------------------------------------------

FIG. 6.2. ELBG codebook optimization

Fig. 6.3. High-level flow-chart of the ELBG block

--------------------------------------------------------------------------------------------------------------------------------

31


--------------------------------------------------------------------------------------------------------------------------------------

FIG. 6.4. Detailed description of the ELBG block

--------------------------------------------------------------------------------------------------------------------------------

32


--------------------------------------------------------------------------------------------------------------------------------------

Chapter – 7

TERMINOLOGY

7.1. Asymmetric Watermarking :

In this case, the detection process (and in particular the detection key) is fully known

to anyone as opposed to blind watermarking where a secret key is required. So here, only a

'public key' is needed for verification and a 'private key' (secret) is used for the embedding

though. Knowledge of the public key does not help to compute the private key (at least in a

reasonable time), it does not either allow removal of the mark nor it allows an attacker to

forge a mark.

7.2. Capacity :

It describes how many information bits can be embedded into the cover data. It also

addresses the possibility of embedding multiple watermarks in one document in parallel.

7.3. Codebook :

In Vector Quatization, k-dimensional Euclidean space Rk, is mapped into a finite

subset C={Ci / i=0,1,…,N-1}. The subset C is generally called a codebook, where C i is a

codeword and N is the codebook size.

7.4. Complexity :

It describes the effort and time we need to embed and retrieve a watermark. This

parameter is essential for real-time applications.

7.5. Copy Protection :

Copy protection attempts to find ways, which limits the access to copyrighted

material and/or inhibit the copy process itself. A recent example is the copy protection

mechanism on DVDs.

7.6. Copyright Protection :

--------------------------------------------------------------------------------------------------------------------------------

33


--------------------------------------------------------------------------------------------------------------------------------------

Copyright protection inserts copyright information into the digital object

without the loss of quality. Whenever the copyright of a digital object is in question, this

information is extracted to identify the rightful owner. It is also possible to encode the

identity of the original buyer along with the identity of the copyright holder, which allows

tracing of any unauthorized copies.

7.7. Cryptography :

It is about protecting the content of messages (their meaning).

7.8. Digital Image :

A digital image a[m,n] described in a 2D discrete space is derived from an analog

image a(x,y) in a 2D continuous space through a sampling process that is frequently referred

to as digitization. The effect of digitization is shown in Figure 10. The 2D continuous image

a(x,y) is divided into N rows and M columns. The intersection of a row and a column is

termed a pixel. The value assigned to the integer coordinates [m, n] with {m=0,1,2,...,M-1}

and {n=0,1,2,...,N-1} is a[m,n]. In fact, in most cases a(x,y) is actually a function of many

variables including depth (z), color ( ), and time (t).

FIG. : Digitization of a continuous image.

7.9. Embedding-key :

It is a secret used to embed the mark in the original image.

7.10. Encryption :

--------------------------------------------------------------------------------------------------------------------------------

34


--------------------------------------------------------------------------------------------------------------------------------------

Encryption protects the images during their transmission. With encryption, an

eavesdropper does not have access to the on-line image when it is transferred. But when the

user has deciphered the image, then this image does not have any copyright protection

anymore.

7.11. Extraction-key :

It is a key used to detect or extract a watermark.

Symmetric watermarking algorithms require use the same secret key for embedding

and extraction. Asymmetric algorithms use a secret key for embedding and a public key for

extraction. Keys are built in such a way that the private key cannot be computed from the

public one.

7.12. Fingerprinting :

Fingerprints are characteristics of an object that tend to distinguish it from other

similar objects. They enable the owner to trace authorized users distributing them illegally. In

the case of encrypted satellite television broadcasting, for instance, users could be issued a set

of keys to decrypt the video streams and the television station could insert fingerprint bits

into each packet of the traffic to detect unauthorized uses.

7.13. Image Authentication :

It is the task of the watermark detector to indicate only those regions that have been

modified to change the content or meaning of the image (tamper detection); the bright pixels

in the images on the right highlight tampered areas in the image.

7.21. Robust Watermarks :

A robust watermark should be stuck to the document it has been embedded in, in such

a way that any signal transform of reasonable strength cannot remove the watermark. Hence a

pirate willing to remove the watermark will not succeed unless they debase the document too

much to be of commercial interest.

7.22. Segmentation :

--------------------------------------------------------------------------------------------------------------------------------

35


--------------------------------------------------------------------------------------------------------------------------------------

In the analysis of the objects in images, it can be distinguished between the objects of

interest and "the rest." This latter group is also referred to as the background. The techniques

that are used to find the objects of interest are usually referred to as segmentation techniques -

segmenting the foreground from background.

7.23. Semi-blind Watermarking :

In some cases you may need extra information to help your detector (in particular to

synchronize its random sequence on the possibly distorted test signal). In particular some

watermarking schemes require access to the 'published' watermarked signal, that is the

original signal just after adding the watermark.

7.24. (Semi-) fragile Watermarks:

A (semi-)fragile watermark is a mark which is (highly) sensitive to a modification of

the stegno-medium. A fragile watermarking scheme should be able to detect any change in

the signal and identify where it has taken place and possibly what the signal was before

modification.

7.25. Signature :

The owner of the image may electronically sign the image (with a hash function and a

signature algorithm), but since the signature is added as a suffix to the image, it can easily be

removed by anyone who gets the image.

7.26. Spatial Watermarks :

Spatial domain, additive watermarking is the same as additive watermarking in any

domain that is a linear transformation of the spatial domain, e.g. Fourier, block DCT,

wavelet, etc. It usually means that someone has created a watermark pattern that has the same

dimensions as the original image and has added the watermark pattern to the image.

7.27. Steganography :

As the purpose of steganography is having a covert communication between two

parties whose existence is unknown to a possible attacker, a successful attack consists in

--------------------------------------------------------------------------------------------------------------------------------

36


--------------------------------------------------------------------------------------------------------------------------------------

detecting the existence of this communication (e.g., using statistical analysis of images with

and without hidden information).

Steganography is a very generic concept that consists in hiding messages in a way that

eavesdroppers or any monitors do not even know that there is a communication and a

message is being sent. Many data hiding techniques were invented for this purpose. Those

techniques inspired the development of watermarking algorithms for copyright protection.

7.28. Transparency :

It relates to the properties of the human sensory system. A transparent watermark

causes no perceptible artifacts or quality loss.

7.29. Vector Quantization :

The objective of VQ is the representation of a set of feature vectors by a

set, Y = {y1 , ..., yNC }, of NC reference vectors in . Y is called codebook and its elements

codewords. The vectors of X are called also input patterns or input vectors. So, a VQ can be

represented as a function: .

7.30. Verification :

It procedure distinguishes between private verification similar to symmetric

cryptography and public verification like in asymmetric cryptography. Furthermore, during

verification, we differ between invertible and noninvertible techniques, where the first one

allows the reproduction of the original and the last one provides no possibility to extract the

watermark without alterations of the original.

7.31. Visible and Invisible Watermarking :

Visibility is a term associated with the perception of the human eye. A watermarked

image in which the watermark is imperceptible, or the watermarked image is visually

identical to its original constitutes a invisible watermarking. Examples include images

distributed over internet with watermarks embedded in them for copyright protection. Those

which fail can be classified as visible watermarks. Examples include logos used in papers in

currencies.

--------------------------------------------------------------------------------------------------------------------------------

37


--------------------------------------------------------------------------------------------------------------------------------------

7.32. Watermarking :


watermarking.

Watermarking is the robust embedding of a copyright information (e.g. time and date,

copyright identifiers or simply a correlation pattern) into a content. This content may be a text

or audio content but most of the time watermarking is applied to still or moving images.

Chapter – 8

EXAMPLES

8.1. Original Lena Image

8.2 Original Robust Watermark

--------------------------------------------------------------------------------------------------------------------------------

38


--------------------------------------------------------------------------------------------------------------------------------------

8.3. Original Semi-fragile Watermark

--------------------------------------------------------------------------------------------------------------------------------

39


--------------------------------------------------------------------------------------------------------------------------------------

8.4. Extracted watermarks under no attacks :

The watermarked images along with extracted watermarks under no

attacks and NHS values are depicted in Fig. 10.

Watermarked Image Under No

attackNHS = 1 NHS = 1

8.6. Spatial-Domain Image Processing Attacks :

Several spatial-domain image processing techniques, including

image cropping, median filtering, blurring, high-pass filtering, contrast

enhancement, and brightness enhancement are performed on the

watermarked image. The attacked images along with extracted

watermarks are depicted in Fig. 12. Except for the case of high-pass

filtering, the robust watermark can successfully survive with NHS > 0.7.

Although the NHS value of the extracted robust watermark in the high-

pass filtering case is somewhat smaller in this algorithm, the information

conveyed therein is still recognizable. For the case of image cropping in

the upper-left corner, the extracted semi-fragile watermark can locate the

cropping position. For each case, the semi-fragile watermark can be used

to verify the authenticity of the watermarked image.

--------------------------------------------------------------------------------------------------------------------------------

40


--------------------------------------------------------------------------------------------------------------------------------------

Image cropping NHS = 0.9109 NHS = 0.8654

Median filtering NHS = 0.9454 NHS = 0.8381

Image Blurring NHS = 0.7732 NHS = 0.5430

Image High Pass filtering NHS = 0.5847 NHS = 0.4790

--------------------------------------------------------------------------------------------------------------------------------

41


--------------------------------------------------------------------------------------------------------------------------------------

Contrast Enhancement NHS = 0.8603 NHS = 0.4880

Brightness Enhancement NHS = 0.8115 NHS = 0.3896

--------------------------------------------------------------------------------------------------------------------------------

42


--------------------------------------------------------------------------------------------------------------------------------------

Chapter – 9

APPLICATIONS

First applications, which came to mind, were related to copyright protection of digital

media. For everyone it is extremely easy to duplicate digital data and this even without any

loss of quality. Similar to the process in which artist artistically signed their paintings with a

brush to claim their copyrights, artists of today can watermark their work and hide for

example their name in the image. Hence, the embedded watermark will allow identifying the

owner of the work.

It is clear that this concept is also applicable to other media such as digital video and

audio. Especially the distribution of digital audio over the Internet in the MP3 format is

currently a big problem. In this scenario digital watermarking may be useful to set up a

controlled audio distribution and provide efficient means for copyright protection, usually in

--------------------------------------------------------------------------------------------------------------------------------

43


--------------------------------------------------------------------------------------------------------------------------------------

collaboration with international registration bodies such as the IDDN-Inter Deposit Digital

Number.

In the field of data security, watermarks may be used for certification, authentication, and

conditional access. Certification is an important issue for official documents, such as identity

cards or passports.

Example of a protected identification card. The identification number "123456789" is

written in clear text on the card and hidden as a watermark in the identity photo.

Digital watermarking allows to mutually link information on the documents. That

means that some information is written twice on the document: for instance, the name of a

passport owner is normally printed in clear text and is also hidden as an invisible watermark

in the photo of the owner. If anyone would intend to counterfeit the passport by replacing the

photo, it would be possible to detect the change by scanning the passport and verifying the

name hidden in the photo does not match any more the name printed on the passport. The

picture below shows a printing machine from Intercard for various types of plastic cards.

--------------------------------------------------------------------------------------------------------------------------------

44


--------------------------------------------------------------------------------------------------------------------------------------

Printing machine for identification cards.

Another application is the authentication of image content. The goal of this

application is to detect alterations and modifications in an image. The three pictures below

illustrate an example of this application. The picture on the left shows an original photo of a

car that has been protected with a watermarking technology. In the center, the same picture is

shown but with a small modification: the numbers on the license plate have been changed.

The picture on the right shows the photo after running the watermark detection program on

the tampered photo. The tampered areas are indicated in white and it can clearly be seen that

the detected areas correspond to the modifications applied to the original photo.

Using digital watermarks for integrity verification.

--------------------------------------------------------------------------------------------------------------------------------

45


--------------------------------------------------------------------------------------------------------------------------------------

Other applications related to conditional access and copy-control are also possible.

For example conditional access to confidential data on CD-ROMs may be provided using

digital watermarking technology. The concept consists of inserting a watermark into the CD

label. In order to read and decrypt the data stored on the CD, the watermark has to be read

since it contains information needed for decryption. If someone copies the CD, he will not be

able to read the data in clear-text since he does not have the required watermark. The picture

below shows an example of a protected CD. To read the data on the CD, the user starts a

program on the CD. This program asks the user to put the CD on the scanner and then reads

the watermark. If the watermark is valid the program decrypts the data on the CD and gives

the user access the clear-text data.

Conditional access to confidential data stored on CD-ROMs.

Chapter – 10

CONCLUSION

An efficient multipurpose watermarking algorithm based on multistage VQ has been

presented. In the this algorithm, the robust watermark is embedded in the first stage using the

robust watermarking method based on index properties [21] and the semi-fragile watermark

is embedded in the second stage using a simple index constrained method. Although the

encoded indices of the attacked watermarked image may be very different from the original

ones, the variance of neighboring indices does not vary too much. The first-stage

watermarking method is, therefore, robust. On the other hand, the second-stage watermarking

--------------------------------------------------------------------------------------------------------------------------------

46


--------------------------------------------------------------------------------------------------------------------------------------

method is based on an index constrained codeword search procedure, in which the index is

modified according to the bit to be embedded. Any change in the encoded indices may

introduce the change in the extracted watermark bit. In other words, the second-stage

watermarking method can tolerate few modifications, so it is fragile to most intentional

attacks. Experimental results demonstrate that this method can be used for copyright

protection by extracting the first-stage watermark, and it can also be used for image

authentication by extracting the second stage watermark.

Compared with existing multipurpose watermarking algorithms, the

advantages of the proposed algorithm are as follows.

1) The proposed algorithm can tolerate rotation attacks with relatively

larger angles.

2) The semi-fragile and robust watermarks are extracted

independently and blindly.

3) Different codebooks are used in the embedding (the two-stage

codebooks) and the extraction (the product codebook) processes.

The final product codebook can be public for users, so the extraction

process sometimes can be performed publicly for special

applications.

4) Because the embedded position in the VQ index for the semi-fragile

watermark is secret, the two stage codebooks used in the

embedding process are secret, and the product codebook for

extraction is encrypted (not the same as that for users), it is hard for

the attacker to forge the valid semi-fragile watermark in the

tampered watermarked image.

5) The developed algorithm can be extended to meet three purposes,

e.g., digital fingerprint, copyright protection and image

authentication, by embedding three watermarks in a three-stage

VQ.

6) The VQ-based watermarking algorithm can reduce the amount of

the data transmitted.

Chapter – 11--------------------------------------------------------------------------------------------------------------------------------

47


--------------------------------------------------------------------------------------------------------------------------------------

FUTURE WORK AND LIMITATIONS

1) The quality of the watermarked image obtained by existing VQ-

based watermarking algorithms is not high enough for copyright

protection. We may use other kinds of vector quantizers or

combine it with DWT or DCT to improve the image quality.

2) The semi-fragile watermark used in the proposed algorithm cannot

tell what kind of attack the watermarked image suffers from.

3) The human visual characteristics are not adopted in the VQ-based

watermarking systems.

4) The extraction is based on the product codebook, so this process is

time consuming if we use the full-search encoding algorithm.

5) From the experimental results, we can see that in the proposed

algorithm, the robust watermark should be embedded in the first

stage for obtaining high quality. How to analyze the embedding

order in general sense is a problem to solve.

--------------------------------------------------------------------------------------------------------------------------------

48


--------------------------------------------------------------------------------------------------------------------------------------

Chapter – 12

REFERENCES

[1] Zhe-Ming Lu, Dian-Guo Xu, and Sheng-He Sun, “Multipurpose Image Watermarking

Algorithm Based on Multistage Vector Quantization,” IEEE Transactions on Image

Processing, vol. 14, no. 6, June 2005

[2] J. J. K. O’Ruanaidh, W. J. Dowling, and F. M. Boland, “Watermarking digital images for

copyright protection,” Proc. Inst. Elect. Eng., vol. 143, no. 4, pp. 250–256, Aug. 1996.

[3] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum

watermarking for multimedia,” IEEE Trans. Image Process., vol. 6, no. 12, pp. 1673–1687,

Dec. 1997.

[4] M. D. Swanson, Z. Bin, and A. H. Tewfik, “Multiresolution scene-based video

watermarking using perceptual models,” IEEE J. Sel. Areas Commun., vol. 16, no. 5, pp.

540–550, May 1998.

[5] G. Voyatzis and I. Pitas, “The use of watermarks in the protection of digital multimedia

products,” Proc. IEEE, vol. 87, no. 7, pp. 1197–1207, Jul. 1999.

[6] S. Pereira and T. Pun, “An iterative template matching algorithm using the chirp-Z

transform for digital image watermarking,” Pattern Recognit., vol. 33, no. 1, pp. 173–175,

2000.

[7] Y. Wang, J. F. Doherty, and R. E. Van Dyck, “A wavelet-based watermarking algorithm

for ownership verification of digital images,” IEEE Trans. Image Process., vol. 11, no. 2, pp.

77–88, Feb. 2002.

[8] P. S. L. M. Barreto, H.Y. Kim, andV. Rijmen, “Toward secure public-key blockwise

fragile authentication watermarking,” Proc. Inst. Elect. Eng., vol. 149, no. 2, pp. 57–62, Apr.

2002.

[9] M. U. Celik, G. Sharma, E. Saber, and A. M. Tekalp, “Hierarchical watermarking for

secure image authentication with localization,” IEEE Trans. Image Process., vol. 11, no. 6,

pp. 585–595, Jun. 2002.

[10] L. Jaejin and S. W. Chee, “A watermarking sequence using parities of error control

coding for image authentication and correction,” IEEE Trans. Consum. Electron., vol. 46, no.

2, pp. 313–317, May 2000.

--------------------------------------------------------------------------------------------------------------------------------

49


--------------------------------------------------------------------------------------------------------------------------------------

[11] D. Kundur and D. Hatzinakos, “Digital watermarking for telltale tamper proofing and

authentication,” Proc. IEEE, vol. 87, no. 7, pp. 1167–1180, Jul. 1999.

[12] C. S. Lu and H. Y. M. Liao, “Multipurpose watermarking for image authentication and

protection,” IEEE Trans. Image Process., vol. 10, pp. 1579–1592, Oct. 2001.

[13] Brad Cox. “Superdistribution: objects as property on the electronic frontier.” Addison-

Wesley, 1996.

[14] Fabien A. P. Petitcolas, Ross J. Anderson, and Markus G. Kuhn. Attacks on Copyright

Marking Systems. In Second International Workshop on Information Hiding, April 1998.

[15] C. Busch and S. D. Wolthusen, “Tracing data diffusion in industrial research with robust

watermarking,” in Proc. 4th IEEE Workshop on Multimedia Signal Processing, 2001, pp.

207–212.

[16] Z. M. Lu and S. H. Sun, “Digital image watermarking technique based on vector

quantization,” Electron. Lett., vol. 36, no. 4, pp. 303–305, Feb. 2000.

[17] Z. M. Lu, J. S. Pan, and S. H. Sun, “VQ-based digital image watermarking method,”

Electron. Lett., vol. 36, no. 14, pp. 1201–1202, Jul. 2000.

[18] Z. M. Lu, C. H. Liu, and S. H. Sun, “Digital image watermarking technique based on

block truncation coding with vector quantization,” Chin. J. Electron., vol. 11, no. 2, pp. 152–

157, Apr. 2002.

[19] J. Minho and K. HyoungDo, “A digital image watermarking scheme based on vector

quantization,” IEICE Trans. Inf. Syst., vol. E85-D, no. 6, pp. 1054–1056, Jun. 2002.

[20] A. Makur and S. S. Selvi, “Variable dimension vector quantization based image

watermarking,” Signal Process., vol. 81, no. 4, pp. 889–893, Apr. 2001.

[21] H. C. Huang, F. H. Wang, and J. S. Pan, “A VQ-based robust multiwatermarking

algorithm,” IEICE Trans. Fund., vol. E85-A, no. 7, pp. 1719–1726, Jul. 2002.

[22] , “Efficient and robust watermarking algorithm with vector quantization,” Electron.

Lett., vol. 37, no. 13, pp. 826–828, Jun. 2001.

[23] Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantizers design,” IEEE

Trans. Commun., vol. 28, no. 1, pp. 84–95, Jan. 1980.

[24] Z. M. Lu, J. S. Pan, and S. H. Sun, “Efficient codeword search algorithm based on

Hadamard transform,” Electron. Lett., vol. 36, no. 16, pp. 1364–1365, Aug. 2000.

[25] Intertrust. Website. “http://www.intertrust.com”, 1997.

--------------------------------------------------------------------------------------------------------------------------------

50


--------------------------------------------------------------------------------------------------------------------------------------

[26] Alma Whitten. “Making Security Usable”. PhD proposal at Carnegie Mellon University,

January 1998. Work in progress.

[27] Robert A. Cinque. Making Cyberspace Safe for Copyright: The Protection of Electronic

Works in a Protocol to the Berne Convention. 18 FORDHAM INT'L L.J., 1995. citing Berne

Convention art 7(6).

[28] Ross J. Anderson and Fabien A.P. Petitcolas. “On The Limits of Steganography.” In

IEEE Journal on Selected Areas in Comunications - Special Issue on Copyright & Privacy

Protection, 1998. http://www.cl.cam.ac.uk/users/fapp2/ papers/ On The Limits Of

Steganography/.

[29] J. J. K. Ruanaidh and Thierry Pun. “Rotation, Scale and Translation Invariant Digital

Image Watermarking”. Submitted to Signal Processing, February 1997.

[30] I. Cox, J. Killian, T. Leighton, and T. Shamoon. “Secure spread spectrum watermarking

for images, audio and video”. In Proceedings of the IEEE Int. Conf. on Image Processing

ICIP-96, pages 243{246, Lausanne, Switzerland, September 16-19 1996.

[31] J. J. K. Ruanaidh, W. J. Dowling, and F. M. Boland. “Phase watermarking of digital

images.” In Proceedings of the IEEE Int. Conf. on Image Processing ICIP-96, pages 239-242,

Lausanne, Switzerland, September 16-19 1996.

[32] Robert A. Cinque. Making Cyberspace Safe for Copyright: The Protection of Electronic

Works in a Protocol to the Berne Convention. 18 FORDHAM INT'L L.J., 1995. citing Berne

Convention art 7(6).

[33] I.J. Cox, J. Kilian, T. Leighton, and T. Shamoon. “A secure, robust watermark for

multimedia.” In ed. R. Anderson, editor, Information Hiding: First Int. Workshop Proc.,

volume 1174 of Lecture Notes in Computer Science, pages 185{206. Springer-Verlag, 1996.

[34] I.J. Cox, J. Kilian, T. Leighton, and T. Shamoon. “Secure spread spectrum watermarking

for images audio and video.” In IEEE Int. Conf. on Image Processing, volume 3, pages

243{246, Lausanne,Switzerland, September 1996.

[35] I.J. Cox, J. Kilian, T. Leighton, and T. Shamoon. “Secure spread spectrum watermarking

for multimedia.” IEEE Trans. on Image Processing, 6(12):1673-1687, 1997.

[36] I.J. Cox and L. Miller Matt. “A review of watermarking and the importance of

perceptual modeling.” In Proc. of Electronic Imageing'97, February 1997.

[37] Ingemar J. Cox and Jean-Paul M.G. Linnartz. “Public watermarks and resistance to

tampering”. In Proceedings of the IEEE International Conference on Image Processing, 1997.

--------------------------------------------------------------------------------------------------------------------------------

51


--------------------------------------------------------------------------------------------------------------------------------------

[38] Mauro Barni, Franco Bartolini, Ingemar J. Cox, Juan Hernandez, Fernando Perez

Gonzalez, “Digital watermarking for copyright protection: A Communication perspective”,

IEEE Communications Magazine, August 2001.

WEB SITES

http://www.ieeexplore.ieee.org

http://www.altern.com/watermark

http://www.cl.cam.ac.uk/~mgk25/stirmark.html

http://www.chez.com/pdufour/ zip/digimarc.zip

http://www.watermarkingworld.org/

http://www.cosy.acat/~pmeerw/watermarking

--------------------------------------------------------------------------------------------------------------------------------

52

Seminar Report on Image Watermarking

Documents