Seminar Report On Image Watermarking 2t6s29

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 . 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, -------------------------------------------------------------------------------------------------------------------------------

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------------------------------------------------------------------------------------------------------------------------------------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 s will behave and think as “logically" as they do. For example image watermarking is used by a wide variety of s and should still be equally applicable and efficient for everybody. Unfortunately this is not the case for today's systems.

1.2.

Interface Concerns

In the “real world" the s 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 enters his original image and by some magic the box outputs the watermarked image. No specific 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 interface (UI) becomes very important: it must present the with a clear model of the effects of watermarking and protect him from misuse. It needs to iron out s 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 understanding but be an implicit property of the software. Therefore the UI becomes a crucial point in any security system.

1.3.

Legal Issues

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------------------------------------------------------------------------------------------------------------------------------------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].

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------------------------------------------------------------------------------------------------------------------------------------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.

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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. -------------------------------------------------------------------------------------------------------------------------------

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------------------------------------------------------------------------------------------------------------------------------------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 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. -------------------------------------------------------------------------------------------------------------------------------

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------------------------------------------------------------------------------------------------------------------------------------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 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. -------------------------------------------------------------------------------------------------------------------------------

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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 . 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 -------------------------------------------------------------------------------------------------------------------------------

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------------------------------------------------------------------------------------------------------------------------------------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.

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------------------------------------------------------------------------------------------------------------------------------------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).

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Chapter - 3 TECHNICAL PROBLEMS OF WATERMARKING

3.1. THE WATERMARKING PROCESS The process of embedding information into another object/signal can be termed as 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 networkaccessible 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 descrambler 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 s 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 s 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 s 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 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 wellknown 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=(x 1,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. k

d ( x, cj ) = ∑ ( x1 − cjl ) 2

(2)

l =1

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.

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------------------------------------------------------------------------------------------------------------------------------------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. 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.

1 m yi = ∑xij m j =1

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 -------------------------------------------------------------------------------------- 16 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------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 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:

1 MSE = M

M

∑ ( xˆ − x ) i

i

i =1

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: −1) PSNR = 10 log 10  ( 2MSE  

n

2

   

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 -------------------------------------------------------------------------------------- 17 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------watermark extraction is dependent on the embedding method. In these algorithms, the codebook is open for s 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. 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 A w * 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 Ci 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

Y = VQ( X ) =

( A / Aw ) −1 ( B / Bw ) −1

U

m=0

=

UVQ( x(m, n)) n =0

( A / Aw ) −1 ( B / Bw ) −1

U

U y ( m, n )

m =0

(3)

n=0

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 1

δ 2 ( m, n) = 

m +1

n +1

∑ ∑y

 9 i =m −1 j =n −1

2

 (i, j )   

 1 m+1 n +1  −  ∑ ∑ y (i, j )   9 i = m−1 j = n −1 

2

(4)

We can obtain the polarities as follows: -------------------------------------------------------------------------------------- 18 ------------------------------------------


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P=

( A / Aw ) −1 ( B / Bw ) −1

U

m =0

U p ( m, n )

(5)

n =0

where 1, δ 2 (m, n) ≥ T p (m, n) =   0, otherwise

(6)

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


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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 -------------------------------------------------------------------------------------- 20 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------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 secondstage error to provide a further refinement and so on. In this paper, we adopt a two-stage vector quantizer as illustrated in Fig. 1.

x

e2

+

•

VQ2

_

VQ1

xˆ1 xˆ1

eˆ 2

xˆ

+ +

xˆ1

• 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 xˆ1 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 eˆ 2 .The overall approximation xˆ to the input x is formed by summing the first and second approximations, xˆ1 and eˆ 2 . 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

-------------------------------------------------------------------------------------- 21 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------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. 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 ------------------------------------------


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

X

•

X1

+

Input Vectors

Division

Codebook C1

_

X’ Composition

Encoded Indices Nearest Neighbor VQ Encoder

•

Output Vectors Normal VQ Decoder

•

P Embedding key2

Index Polarities Computation

WRP WR key1

Permutation

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. -------------------------------------------------------------------------------------- 23 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------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 embedded watermark is to be output. These secret keys are ed 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 -------------------------------------------------------------------------------------- 24 ------------------------------------------


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

µ(m, n) =

1 m +1 n +1 ∑ ∑ y (i , j ) 9 i = m −1 j = n −1

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

σ2 (m,n) =

1 m +1 n +1 ∑ ∑ y(i, j ) - µ2(m,n) 9 i = m −1 j = n −1

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 operationkey1=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 ------------------------------------------


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

WF

Division

X1

+

Encoded Indices Index Constrained VQ Encoder

Codebook C2

X’

XW

Input Vectors

Watermarking Position key3

+

Output Vectors

’ 1

X

Composition

Normal VQ Decoder

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


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

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 s. Codebook Cu Codebook C key4

Inverse Permutation Input Vectors

Xw Division

Normal VQ Encoder

Encoded Indices

Segmentation Position key5 Indices of Stage1 Indices of Stage2

Watermarking Position key3

Index Polarities Computation

Index Segmentation

Bit judgment Extracted Bits

key2

Extracted Semi-Fragile

XOR Composition key1

Watermark WEF

Extracted Robust Watermark WER

Inverse Permutation

FIG. 4. Watermark Extraction Process In addition, because the s 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 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------s. In order to make the embedding algorithm more secretly, we can also permute the product codebook and then publicize the permuted codebook Cu for s. 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 semifragile 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 twostage 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 ------------------------------------------


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

Start

Initial codebook (Y0)

M=0; D-1 =+∞

New partition calculation Sm=P(Ym)

Distortion (Dm) calculation

(Dm-1 -Dm) / Dm <ε

ELGB Block Final codebook (Ym); New codebook calculation Ym+1 =X(Sm’) End m=m+1

-------------------------------------------------------------------------------------- 30 ------------------------------------------


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

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 Ci 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 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 s distributing them illegally. In the case of encrypted satellite television broadcasting, for instance, s 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 x ∈ X ⊆ R k by a set, Y = {y1 , ..., yNC }, of NC reference vectors in R k . 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 → Y . represented as a function: q : X  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 : The process of embedding information into another object/signal can be termed as 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 NHS = 1

attack

NHS = 1

8.6. Spatial-Domain Image Processing Attacks : Several spatial-domain image processing techniques, including image cropping, median filtering, blurring, high- 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- filtering, the robust watermark can successfully survive with NHS > 0.7. Although the NHS value of the extracted robust watermark in the high 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 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

-------------------------------------------------------------------------------------- 41 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------Image High filtering

NHS = 0.5847

NHS = 0.4790

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 -------------------------------------------------------------------------------------- 43 ------------------------------------------


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

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 port 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 port by replacing the photo, it would be possible to detect the change by scanning the port and ing the name hidden in the photo does not match any more the name printed on the port. 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 starts a program on the CD. This program asks the 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 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 method is based on an index constrained codeword search procedure, in which the index is -------------------------------------------------------------------------------------- 46 ------------------------------------------


------------------------------------------------------------------------------------------------------------------------------------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 s, 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 s), 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 FUTURE WORK AND LIMITATIONS -------------------------------------------------------------------------------------- 47 ------------------------------------------


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


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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.” AddisonWesley, 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/s/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

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