13

Chapter 2

An Overview of Digital Watermarking

Digital watermarking involves embedding of a watermark containing significant

information about the work or its owner in digital multimedia contents in a way

that the presence of the watermark can be detected despite various intentional or

unintentional manipulations. While the field of digital watermarking is relatively

new compared to the use of traditional centuries old visible watermarks, the

theories and technologies behind it are not [2]. Digital watermarking techniques

are based on the principles and methods from a wide range of diverse disciplines

like steganography, spread spectrum communications technology and the

perceptibility concept and noise theory. Steganography is the practice of

embedding secret information by imperceptibly altering digital contents so that the

information can be extracted only by the intended receiver. Both steganography

and digital watermarking are the sub-fields of information hiding and make use of

the principles of spread spectrum communications technology, human visual

perceptibility concepts and fundamentals of noise theory to embed the watermark

in the form of narrowband signal over the larger bandwidth such that it is neither

perceptible nor statistically noticeable [26]. Despite these similarities,

steganography and watermarking are different in terms of applicability and

preferences. The basic and sole purpose of steganography is to covert

communication whereas digital watermarking is used for a variety of applications

related to intellectual property rights protection including copyright protection,

content authentication and piracy control. Further, various steganography

techniques strive for imperceptibility and high embedding capacity but give less

importance to robustness, whereas robustness is crucial for many digital

watermarking systems.

14

2.1. A Generic Model for Digital Watermarking System

Before describing the generic watermarking model, we define some standard

terminology and notations used as follows.

i. Media refers to any digital contents including text, images, videos and

audio clips.

ii. Host media )(I is the original digital signal which is to be watermarked. It

is also referred to as cover media, source data or original data.

iii. Watermark )(w can be any signal which is embedded into the cover. It

may be an identification code or message containing significant

information about the origin, owner, contents, authorised receiver and

usage of the cover. In certain cases, it may be a logo or some randomly

generated sequence. In our thesis, it is a pseudo-randomly generated binary

sequence.

iv. Embedding process is the process of inserting the watermark signal into

the cover media.

v. Watermarked media )( wI is the output watermarked media obtained after

embedding the watermark into the cover.

vi. Communication channel refers to the technology used for the

transmission of watermarked media from source to destination. The

communication channel is prone to attacks and may cause distortions in

the transmitted media.

vii. Attacks and threats are the distortions to the watermarked media. These

distortions may be intentional or unintentional. Some commonly occurring

unintentional distortions are due to lossy compression techniques, noise

added by the communication channel and other signal processing

transformations like analog to digital conversion and vice-versa. The

intentional distortions include geometric transformations and other

malicious changes to breach the security and authenticity of watermark.

viii. Attacked watermarked media )( wI is the possibly attacked or manipulated

version of watermarked media due to various intentional or unintentional

attacks.

15

ix. Detection is the process of asserting the presence of the watermark signal

in the received media. Given a possibly attacked watermarked media, the

response of the detector is binary, indicating ‘present’, if the media is

found watermarked or ‘absent’ otherwise.

x. Extraction is the process of revealing the watermark embedded in the

received media. In addition, the extraction process may have to decode and

validate the information extracted before it can be used as evidence in the

court of law.

A generic digital watermarking system consisting of an embedding process

for inserting the watermark signal at the sender side and a detector or extractor at

the receiver side is shown in Fig. 2.1. The watermark embedding process takes

two inputs. One is the watermark we want to embed and the other is the cover

media in which watermark is to be embedded. The embedding process inserts the

watermark into cover media and produces watermarked media which is

transmitted over a noisy and hostile channel. At the receiver side, there may be a

detector or extractor. While a watermark detector only verifies the presence of the

watermark which may be non informative in nature, an extractor has the more

challenging job of revealing the embedded watermark from a possibly corrupted

received media. The watermark detector or extractor at the receiver side can be

designed with or without some prior information about the watermark signal or

the original cover media. Since it is assumed that the communication channel can

be noisy and prone to security attacks, hence the digital watermarking techniques

should be resilient to both unintentional manipulations and security attacks.

Fig. 2.1. A generic digital watermarking system.

Extractor

Embedding

Process

Communication

Channel

Watermark

)(w

Host media

)(I

Watermark

)(w

Attacks and Threats

Watermarked

media

)( wI

Detector Binary

response Attacked

watermarked

media

)( wI

16

The general watermarking model presented in Fig. 2.1 is defined for all

types of digital contents including audio tracks, images and video clips. Generally,

an audio signal is defined as the one-dimensional wave signal, an image has two-

dimensions each along the horizontal and vertical axes and video is considered

three-dimensional with ‘time’ as its third dimension. With proper transformations,

a one-dimensional audio signal can be straightforwardly mapped to an image like

two-dimensional signal while videos can be considered as a sequence of still

images. It is worth mentioning here that the digital video is generally stored and

distributed in compressed format (e.g., MPEG-2, MPEG-4 etc.) in which the

compression algorithms take advantage of temporal redundancy in the video.

Thus, while embedding watermarks in a video, one have to ensure that the

watermark remains unaffected by compression of the raw video signal and should

not alter the bit-rate of the video.

Our research work will primarily focus on still images because of their

easy extendibility for other digital media like video and audio signals. Thus, the

concepts and work presented henceforth specifically apply to image

watermarking. We will use the term ‘watermarking’ with an implicit reference to

image watermarking while the term ‘digital watermarking’ will be used for the

generalisation of the concept wherever and whenever necessary.

2.2. Properties of a Watermarking System

An image watermarking system can be characterised by a number of properties

associated with its embedding or detection process and the role played by the

watermark for specific applications. Following are some of the desirable

properties of an effective image watermarking system.

2.2.1. Embedding Effectiveness

The effectiveness of an image watermarking system is defined as the probability

that the output generated by the embedding process is the watermarked media. In

other words, it is the probability of detecting the watermark immediately after

embedding [3]. Though 100% effectiveness is desirable for a good watermarking

system, the level of effectiveness may vary for specific application requirements.

17

2.2.2. Visual Imperceptibility

The embedding of the watermark signal causes visual degradation to the host.

Many watermarking systems require that these degradations must be

imperceptible or perceptually invisible in order to maintain the aesthetic value.

Visual imperceptibility is also desirable for preventing unauthorised revelations of

the information and ensuring watermark security. In order to achieve good visual

imperceptibility, the digital watermarking scheme takes advantage of the human

visual system (HVS) models. According to these models, human eyes are less

sensitive to the changes made in the highly textured complex regions compared to

the flat monotonous regions of the image. Thus, textured regions are considered

more suitable for embedding larger and stronger watermarks.

2.2.4. Robustness

Watermark robustness is defined as the ability to detect the watermark despite

various geometric and signal processing transformations. It is an essential

property when watermarking is used for the protection of copyrights and owner

identification. However, not all applications require robustness against all possible

attacks. Certain applications pertaining to integrity checking must not be robust

against malicious distortions, but should survive non-malicious distortions like

compression and transmission noise. Fragile watermarks must be sensitive to even

a slightest distortion.

2.2.3. Embedding Capacity

Embedding capacity or the data payload of a watermarking scheme refers to the

number of bits that can be embedded into the host image without significant loss

of robustness and visual imperceptibility. Different applications require varying

amounts of data to be embedded. For example, a copy control application may

require just 4-8 bits of watermark to be embedded while image authentication

applications specific to medical imaging needs high capacity embedding up to

thousands of bits. A typical application may require to embed between 60 and 100

bits so as to uniquely associate images with buyers and sellers [27].

18

2.2.5. Detector Performance

An effective watermarking system requires a detector that provides reliable results

under all circumstances. Ideally, a reliable detector must be able to detect the

watermark from watermarked media despite the severity or the number of

intentional or un-intentional attacks and should report failure if it does not contain

one. However, this might not take place in practice resulting in two kinds of error

probabilities. These are false positive and false negative rates. A false positive rate

is the probability of detecting the watermark from an image that actually does not

contain one while the false negative rate is defined as the probability of not

detecting the watermark from an image that actually contains one. A digital

watermarking system must have an infinitesimal false positive rate and false

negative rate [3]. High false positive can cause serious trouble for a copy control

application as it might lead to a false accusation of theft in case of transaction

tracking system and on the other hand, a false negative may deprive an

authenticated owner of his intellectual rights. Similarly, in case of fragile

watermarking, if a detector fails to report the malicious changes made to the

multimedia contents presented as evidence in the court of law, it may lead to

inappropriate judgments resulting in the acquittal of criminals or punishing the

innocent.

2.2.6. Watermark Security

Watermark security is desirable for resisting intentional attacks such as

unauthorised embedding for forgery, watermark detection or watermark removal

by an unauthenticated person. It requires that the locations where the watermark is

embedded and the amount of information embedded should be secret and must not

be detectable by any unauthorised person who does not have the secret key.

Security can be implemented by ensuring visual imperceptibility, use of keys and

further encrypting the watermark before embedding. So far security was

considered more of a concern in steganography than in watermarking, but there

are applications like broadcast monitoring that may require high levels of security.

In general, the required level of security depends on the type of application for

which an image watermarking system is being designed.

19

2.2.7. Uniqueness

Uniqueness allow multiple watermarks to coexist and survive in the watermarked

image. It is desirable in certain applications where content distribution may

include a number of intermediaries before reaching the end-user or there are

multiple distributors. This property not only ensures the identification of the

distributors, but also enhances the security of watermark against the forgery

attack.

2.2.8. Computational Cost

For the commercial viability of a watermarking system, the insertion and detection

of watermark must be cost-effective in terms of speed and hardware required for

its implementation. Some watermarking applications like broadcast monitoring

need to perform in an almost real-time environment, while a detector for the proof

of ownership may take several days to find the watermark. Another issue

determining the computational cost requirement is whether the embedding or the

detection processes are to be implemented using special hardware, software

applications or plug-in.

2.2.9. Scalability

Another desirable property of a watermarking system is its scalability with each

generation of computers. As the technology is advancing with a tremendous pace,

the present generation detector might be computationally inexpensive and portable

but might not be as reliable as next generation detectors that are capable of

handling more severe forms of attacks.

2.3. Taxonomy of a Watermarking System

We classify an image watermarking system depending on the specific

requirements of the application and the properties of watermark embedding and

detection processes. Three significant properties identified with the watermark

embedding process are imperceptibility, domain for embedding and capacity

while other significant properties like robustness and blind vs. non-blind detection

are associated with specific application area and nature of the detection process. A

20

generalised classification tree for the watermarking system is as shown in Fig. 2.2.

Our research deals with the specific type of a watermarking system characterised

by the shaded parts of the tree.

Fig. 2.2. Taxonomy of an image watermarking system.

2.3.1. Robust, Fragile and Semi-Fragile Watermarking

Depending on specific application requirements, an image watermarking system

can be categorised as either robust or fragile. Robust watermarking is used for

applications in which watermark is embedded for the purpose of owner

identification, proof of ownership and copyright protection. A robust watermark

must be able to survive various attacks such as filtering, additive noise, lossy

compression and geometric distortions (rotation, scaling, translation, etc.). On the

other hand, some applications like authentication or integrity verification

applications require a watermark to survive only if the watermarked images are

intact and any image processing operation should cause the watermark to be lost.

Such watermarking systems use fragile watermarks. A fragile watermarking

Classification of Image watermarking

Embedding

process

Detection

process

Embedding

domain

Perceptibility

Capacity

Spatial

Invariant

Transform

Visible Invisible Zero bit (Non-

informative)

Multi bit (Informative)

Blind Informed

Application

specific

Robust

Semi

Fragile

Fragile

21

system must report a failure even at the slightest tampering. However, not all

applications can be categorised strictly as robust or fragile. This leads to the field

of semi-fragile watermarking, which survives minor transformations such as lossy

compression, but is invalidated by major changes.

2.3.2. Invisible and Visible Watermarking

An image watermarking scheme that tries to embed the watermark in such a way

that it does not alter the human perception of the cover media and can be detected

only by using appropriate signal processing techniques is called invisible or

imperceptible watermarking. Invisible watermarking generally takes advantage of

the limitation of human perception [8]. However, some applications may require

the authenticated owner logo or information to be displayed on the host image

without obscuring its contents. Such a watermarking system falls into the category

of visible watermarking.

2.3.3. Spatial, Transform and Invariant Domain Watermarking

An image watermarking scheme can be also categorised on the basis of the

domain used for embedding the watermark as follows.

i. Spatial Domain: The watermark signal can be embedded into the host

image by directly modifying the intensity of pixels. Some initial work in

the field of digital watermarking is found in the spatial domain [28-37].

Later, it was observed that spatial domain methods are less resilient to

common signal processing transformations and are not suitable for robust

watermarking [38]. However, due to the simplicity of approach spatial

domain techniques are still proposed for some watermarking applications.

ii. Transform Domain: The watermark signal in the transform domain is

embedded by modifying the transform coefficients of an image and inverse

transform is applied to obtain the watermarked image. Various transforms

such as discrete Fourier transform (DFT) [39-41], Fourier-Mellin

transform (FMT) [42-43], discrete cosines transform (DCT) [44-48],

Contourlet Transform [49], discrete wavelets transform (DWT) [50-51],

fractal transform [52], Bandelet transform [53] and more recently random

22

fraction Fourier transform (RFrFT) [54] have been used for digital

watermarking. The performance of transform domain watermarking

methods generally depends on the features of the particular transform used

for embedding.

iii. Invariant Domain: Invariant domain is preferred for embedding the

watermark signal as it provides greater robustness to geometric attacks.

Moment invariants for robust watermarking were initially proposed by

Alghoniemy and Tewfik [56-57], using two of the seven well-known

geometric moment invariants given by Hu [58] to embed a non-

informative watermark. Since then, many invariant moment and

transforms such as polar harmonic transform (PHTs) [55], complex

moments (CMs) [59], Gaussian-Hermite moments (GHMs) [60],

Krawchouk moments (KMs) [61-63], Legendre moments (LMs) [64],

Tchebichef moments (TMs) [49, 65-66], volume moments (VMs) [67],

wavelet moments (WMs) [68] and Zernike and pseudo-Zernike moments

(ZMs/PZMs) [69-82] have been investigated for digital watermarking.

These schemes are essentially based on invariance properties of image

features and are generally referred to as invariant watermarking

techniques.

2.3.4. Informative and non-Informative Watermarking

In an informative watermarking, the watermark contains some significant

information about the owner, recipient or the contents of the image. Thus, the

embedding process needs to embed multi-bit data into the host image that can be

extracted later to prove the authentication of watermark. A watermarking system

is said to be non-informative or zero-bit watermarking system if there is only one

possible watermark and the detector determines whether or not that the watermark

is present. An informative watermarking may offer low or high capacity

embedding. Most of the watermarking systems need to embed only an

identification code and thus require low embedding capacity. High capacity

watermarking systems are typically desirable for applications related to secure

media distribution [83], thumbnail embedding for authentication, auxiliary data

embedding [84] and medical image watermarking [85].

23

2.3.5. Blind and Informed Watermarking

An image watermarking system may be designed to have either a blind or

informed detector. A blind detector is capable of detecting the watermark without

requiring an access to the original image or watermark embedded. A

watermarking system using blind detector is also known as a public watermarking

system. An informed detector requires access to some information about the host

or watermark signal for the extraction and leads to a private watermarking system.

In general, designing a blind detector is more challenging compared to the

informed detector due to lack of any priori information about the host or

watermark signal.

2.4. Watermark Attacks and Threats

A watermarked image is likely to be subjected to attacks and threats during

transmission or transportation between the client and the copyright authority that

can either remove the watermark or render it unusable for owner identification.

Various types of attacks and threats have been reported in literature and many

more are appearing. We distinguish between the terms “attack” and “threat” on

the basis of severity of criminal nature involved. An attack is the “distortion” to

the signal due to various image processing operations and watermark robustness is

the ability to withstand these attacks. On the other hand, a threat is the “malicious

manipulation” that exploits the technical knowhow of the watermarking system

and concerns the watermark security issues. A robust watermark may not be

secure enough to survive the malicious manipulations and similarly, a secure

watermark may be sensitive to attacks. The research activities on counterfeiting

threats involve issues related to watermark security including watermark

estimation, watermark removal or modification while the major motivation behind

robust watermarking is to ensure the survival of watermark under various image

processing attacks.

The broad classification scheme for watermark attacks and threats is

presented in Fig. 2.3. In general, we classify the attacks into two categories,

namely, un-intentional attacks and intentional attacks. Unintentional attacks

typically include inherent degradations that occur during compression,

24

transmission noise or conversion while intentional attacks include the direct

manipulation of watermarked image with a specific purpose of removing the

watermark or preventing its correct detection. Intentional attacks are further

grouped into two categories called common image processing and malicious

attacks. Common image processing attacks attempt to impair the embedded

watermark by the manipulation of the entire watermarked image without any

attempt to identify or isolate the watermark. Some common image processing

techniques include geometric transformations, image degradation or enhancement.

Malicious attacks or threats are the manipulations for the purpose of forgery or

watermark removal and are discussed here only for the sake of completeness.

Fig. 2.3. Classification of watermarking attacks.

Attacks and Threats

Intentional

Forgery Image

Enhancement

Removal

Mosaic

SWICO TWICO

Other geometric distortions

Patching Statistical averaging

Collusion attack

Image Degradation

Malicious

Unintentional

Compression Conversion

Geometric transformations

Rotation

Scaling Translation

Noise

Transmission Noise

Flipping

Common Image Processing

Spatial filtering

Cropping

Other manipulations

25

2.4.1. Unintentional Attacks

A watermarked image may undergo changes during some innocent image

processing operations. These may be unintentional distortions due to the inherent

nature of the transmission media, compression or conversions described as

follows.

i. Compression: Image compression is done to reduce the storage space and

save the cost of transmission. The compression can be achieved using

lossless or lossy techniques. Lossless image compression techniques

provide a lower compression ratio but watermark information is recovered

with an inverse operation. Lossy compression techniques like JPEG are

widely used for images because they provide high compression ratio and

are supported by all web browsers. These techniques, however, reduce the

colour levels and the bandwidth and make irreversible changes causing

removal of the watermark.

ii. Transmission Noise: Transmission noise refers to the analog interference

added by the communication channel. This can weaken the strength of

signal, resulting in loss of information and inability to detect the

watermark. These interferences are generally modeled as additive white

Gaussian noise (AWGN) and can be suppressed by de-noising techniques

at the receiver side. However, the techniques used to reduce the noise also

tend to remove the useful information embedded in the host, resulting in a

new kind of attack on robust watermarking.

iii. Conversion: A digitally stored image can be printed on analog media and

then scanned back into the computer. An analog to digital conversion

followed by vice-versa degrades the quality of the watermarked image and

renders it useless for watermark detection.

Another common source of conversion attacks is colour reduction.

After applying the colour reduction, lesser number of bits are used to

identify the colours. This causes reduction in colour levels and steps

between colours become more noticeable, resulting in enhanced

appearance or disappearance of the watermark depending on its

robustness.

26

2.4.2. Image Degradation Attacks

An image can be degraded by removing or replacing its parts in order to break the

watermark. Some common examples of image degradation attacks are:

i. Noise: Adding Gaussian noise, either in the random or uniform fashion, is

simple and supported by many image processing software. The amount of

noise is controlled by its mean and variance. The watermark detector may

not be able to detect the watermark in the presence of additive noise.

ii. Cropping: Cropping refers to the removal of horizontal and/or vertical

array of pixels from the watermarked image. The number and the locations

of rows and columns removed may be arbitrary. However, only a marginal

number is sufficient to destroy the synchronisation of watermark

embedded resulting in failure to detect the watermark.

iii. Patching: Patching is another kind of attack in which a rectangular area of

watermarked image is replaced with a perceptually similar patch. Both

cropping and patching are highly effective if the watermark is not present

in the whole image or when the whole image is required for watermark

detection.

2.4.3. Geometric Transformations

Geometric transformations destroy the synchronisation of the watermark.

Rotation, scaling and translation (collectively known as RST) are three basic

forms of geometric transformations that can be applied to invalidate the claims of

ownership due to the failure of detection process. A geometric transformation can

either be applied globally (on the entire image) or locally (on some selected

regions of the image) resulting in two different kind of challenges for robust

watermarking. In the worst case, an image may be attacked with both local and

global transformation. Following is the brief description of common geometric

transformations.

i. Rotation: A rotation realigns the horizontal or vertical features of the

image. Rotating an image by a small angle does not make any significant

27

difference in the perceptual quality of a watermarked image, but can make

the watermark un-detectable.

ii. Scaling: Scaling refers to changing the dimensions of the image. It can be

further divided into two groups, uniform and non-uniform scaling. Under

uniform scaling, same scaling factor is used along each direction without

changing the aspect ratio. Non-uniform scaling uses different factors along

horizontal and vertical directions, causing change in the aspect ratio. Most

of the watermarking schemes are found resilient only to uniform scaling.

iii. Translation: It refers to the horizontal, vertical or diagonal shift of pixels

in a circular fashion so that there is no perceptual or statistical loss to the

contents of the image. Translation may occur implicitly due to some

attacks such as after print and scan attack or can be applied explicitly to

destroy watermark synchronisation.

iv. Flipping: An image can be flipped horizontally or vertically depending on

the symmetry. Flipping does not cause any statistical loss to the image

contents. Although resilience to flipping is usually simple to implement,

not all watermarking schemes survive it.

v. Other Geometric Distortions: Some severe forms of geometric

distortions can be applied by combining two or more basic geometric

transformations. For example, shearing or titling a popular geometric

attack which is a combination of translation and scaling. Other examples

of geometric distortions are pixel permutations, sub sampling, re-sampling,

removal or insertion of either pixels or pixel cluster.

It may also be noted here that if a watermarked image is known to have

undergone a basic geometric transformation, then applying an inverse geometric

transformation generally does not produce the un-attacked version. In fact,

applying inverse transformations also add distortions due to pixel re-sampling and

interpolation processes. It can be easily observed that a rotation by an angle of

in the clockwise direction followed by another rotation in counter-clockwise

direction by the same angle will not produce the original image. Similar results

can be observed for scaling and other geometric transformations except for

flipping because it has an exact geometric inverse.

28

2.4.4. Image Enhancement Attacks

Most of the image enhancement techniques applied through convolution

operations destroy the synchronisation of watermark embedded and result failures

during detection. Some of the commonly used image enhancement techniques are:

i. Spatial Filtering: A spatial filter creates a new image by taking linear or

non-linear combination of surrounding pixels. There are various types of

linear and non-linear filters and each filter is capable of producing

different manipulated version of the watermarked image. For example, an

edge enhancement filter typically amplifies the luminance of an image and

subtracts shifted versions of the surrounding, resulting in redundancy

cancellation and exaggeration in the randomness of the embedded

watermark [86]. On the other hand, smoothing and low pass filters often

decrease the luminance of image and thus reduce the reliability of a

correlation-based watermark detection scheme.

ii. Other Image Enhancement Operations: Some other examples of image

enhancement operations in which an image may undergo mathematical

transformations include histogram equalisation, pixel quantization,

sharpening, Gamma correction and jitter attack.

2.4.5. Forgery or Ambiguity Attacks

These are the attacks that attempt to confuse the detection process by producing

fake original data or fake watermarked data. This can be done by ghost searching

where an attacker tries to find a ghost watermark and claims it as his own

watermark or attempting to discredit the watermark authority by embedding one

or more additional watermarks such that it is unclear which among them is the

authoritative watermark. A number of methods are discussed by Craver et al. [86]

in which watermarks that are used to identify the owner might be thwarted by

embedding conflicting watermarks. Forgery attacks are also known by several

other names in the literature such as deadlock attacks, inversion attack, counterfeit

attacks, fake-watermark attacks or fake-original attacks. Some of the popular

forgery attacks are:

29

i. SWICO (Single watermarked image counterfeit original) attack: It is a

kind of forgery attack in which an attacker does not remove the originator

watermark, but embeds his own fake watermark to confuse the detector

[86]. SWICO attack can be shown as in Fig. 2.4. The only criteria while

choosing the fake watermark is that insertion of fake watermark into the

fake original must produce the original watermarked image. Thus, SWICO

attack requires ghost searching.

Fig. 2.4. SWICO attack: The attacker computes an image wI and fake

watermark w , such that embedding w in wI yields wI .

ii. TWICO (Twin watermarked images counterfeit original) attack: As

illustrated in Fig. 2.5 in a TWICO attack, the attacker can compute any

watermark and its corresponding fake original image even if the

embedding process yields fake watermarked different from the original

watermarked. The forgery attack is still successful. Both SWICO and

TWICO are sometimes referred to as jamming and saturation attacks or

IBM attacks [87] that do not try to alter the original watermark but embed

fake watermarks to counterfeit the originals.

Attacker

embedding

process

Fake

original

)( wI

Original

watermark

)(w

Fake

watermark

)(w

Watermarked

image

)( wI

Sender

embedding

process

Original

image

)(I

30

Fig. 2.5. TWICO attack: The attacker computes an image wI and fake

watermark w , such that w is present in wI even if wI and wI

are different.

iii. Mosaic Attack: In order to confuse the web crawlers that check the

downloaded contents for a client’s watermark, a mosaic attacker first

chops an image into number of smaller sub-images and then embed them

one after the other in a web page. A web browser renders juxtaposed sub

images stuck together as a single image, so the result is identical to the

original image, but the detector fails to detect the presence of watermark

due to small size of each sub-image [38]. Mosiac attack can be considered

as an extreme case of cropping in which a detection ambiguity is generated

through image segmentation.

2.4.6. Removal Attacks

These are the attacks that analyse the watermarked data, estimate the watermark

or the host data in an attempt to split the watermarked data into host data and the

watermark. These attacks pose serious threats to the intellectual rights. Two

extensively reported removal attacks in the literature are:

i. Statistical Averaging Attack: De facto a creator or copyright owner

always embeds unique watermark in his entire work. Thus, if an attacker

has access to several watermarked images from the same origin, he may

try to estimate the watermark and subtract it from the watermarked image.

Attacker

embedding

process

Fake

original

)( wI

Original

watermark

)(w

Fake

watermark

)(w

Watermarked

Image

)( wI

Sender

embedding

process

Original

image

)(I Watermarked

version

of fake

original

)( wI

31

In fact, the attacker tries to estimate a generic watermark ),( wIfw w not

depending significantly on wI . Such an attack is particularly dangerous as

once estimated, w can be used to remove a watermark from any arbitrarily

image from the same origin without any further effort for each new image

to be cleaned [88].

ii. Collusion Attack: Contrary to averaging attack, the attacker in the

collusion attack has access to several versions of the image nwww III ,,,

21

each with a different watermark but each perceptually equivalent to say an

image 0I . By analysing these versions and suitable reverse engineering

methods, an attacker can learn about regions of image that are equivalent

to un-watermarked version 0I . The attack is called collusion attack as

several watermarked images need to be collided to construct the

corresponding un-watermarked version. The only challenge during

collusion attack is the unavailability of information required to detect the

presence of watermark that gives rise to the uncertainty in the success of

collusion attack [88].

It may be noted that the transitions between the groups is sometimes fuzzy.

For example, choosing high compression ratio may be a deliberate attempt to

destroy the watermark. Thus, a lossy compression can be either intentional or un-

intentional attack. Similarly, de-noising and certain non-linear filter operations

can act as removal attacks [89]. However, in an attempt to arrange in the

ascending order of technical knowhow required and gravity of the threat posed to

a watermarking system, various attacks can be tentatively ordered as un-

intentional attacks, image degradation attacks, image enhancement attacks,

geometric attacks, forgery attack and removal attacks.

It is also worth mentioning here that our classification includes attacks

specifically to image watermarking and may not incorporate all possible attacks

related to other digital media watermarking. For example, many DVD players and

gaming devices such as Sony Play Station 2 (PS/2) and Play Station Portables

(PSPs) nowadays use digital watermarking to prevent piracy and copy control.

These devices come with built-in watermark detection software. However, there

32

exist attacks in the form of hardware and software cracks that tamper with the

output of the watermark detector in such a way that copy control mechanism sees

‘no copy watermark’ and allow the device to play the pirated contents.

Surprisingly, many of such soft cracks are available on the Internet and can be

easily downloaded and installed for playing pirated games.

2.5. Evaluating an Image Watermarking System

Fair evaluation of any method or system implementation is essential for its

success and accreditation. There exist many standard quantitative measures and

metrics that can be used to evaluate the performance of a digital watermarking

system and compare it with other schemes. In this section, we present a brief

survey of various standard quantitative metrics found in the literature and those

used in this thesis to measure four key properties of a watermarking system,

namely – visual imperceptibility, robustness, capacity and computational cost.

2.5.1. Visual Quality Metrics

Visual quality metrics are used to quantitatively measure the visual

imperceptibility of the watermarked images. The simplest way to access the

quality of a watermarked image is to apply a human-based subjective evaluation

method on a large number of images. However, due to huge effort, time and

subjective judgments involved, an automated quantitative measurement of

imperceptibility is preferred. Visual quality of the watermarked images can also

be measured using quantitative metrics. These quantitative measures are based on

difference distortion measures [90] or similarity based measures [91]. The

similarity based measures such as Structured SIMilarity (SSIM) and Feature

SIMilarity (FSIM) directly evaluate the structural changes between two complex-

structured signals and do not attempt to predict image quality by accumulating the

errors due to noise added to an image [91]. Therefore, difference distortion

measures such as such as mean square reconstruction error (MSRE) and peak-

signal-to-noise ratio (PSNR) are widely used for evaluating the quality of

watermarked images. These measures are also more appealing because they are

simple to calculate, have clear physical meanings, and mathematically convenient

in the context of optimization.

33

i. Mean Square Reconstruction Error (MSRE): The quality of a

reconstructed or watermarked image can be measured with a widely used

pixel-based difference distortion metric, known as mean square

reconstruction error (MSRE), represented by and defined as:

1

0

1

0

2

1

0

21

0

),(

),(ˆ),(

M

i

N

k

N

k

M

i

kif

kifkif

(2.1)

where ),( kif is the original image of size NM pixels with 8-bit

grayscale colour space, ),(ˆ kif is its reconstructed or distorted version. A

smaller value of MSRE is an indicative of higher quality of reconstructed

image while 0 is obtained when ),(ˆ kif is the true copy of ),( kif .

ii. Peak Signal-to-Noise Ratio (PSNR): Another generally deployed metric

for evaluating the imperceptibility of a watermarked image, ),(ˆ kif , with

respect to original input image, ),( kif , is peak signal-to-noise ratio given

by:

1

0

1

0

2

2

10

),(),(ˆlog10

M

i

N

k

MAX

kifkif

INMPSNR (2.2)

Here, MAXI is the maximum gray level of the image and for an 8-bit

grayscale 255MAXI . PSNR is measured in decibels (dB). While PSNR

value of dB44 is acceptable for most of image watermarking

applications, higher values are desirable for better imperceptibility.

Many researchers [25] are against the use of difference distortion metrics

and propose quality metrics based on perceptual phenomena of human visual

systems and multi-channel model of the human spatial vision. It is argued that

pixel-based metrics are not correlated with human vision model and thus result in

misleading quantitative distortion measures. Thus, a higher value of PSNR or

34

smaller MSRE is not always an indicative of better visual imperceptibility. We

find that the criticism is valid under some circumstances when the images are

distorted using different methods. However, for the comparison purposes, when

the source of distortion is similar, quantitative distortion measures are simple and

effective. This can be explained with the help of an example. For illustration we

choose the gray scale image Lena given in Fig. 2.6(a) as it is widely used by

researchers in the field of image processing. First, we marked the original image

with a visible character ‘C’ at the lower right corner of the image and then using

an invisible watermarking scheme given by Cox et al. [3] in the DCT domain with

two embedding strengths and . The output images are shown in

Fig. 2.6 (b), (c) and (d) respectively and the computed values of PSNR and MSRE

are shown below them. While comparing the marked image of (b) with (c) and

(d), we observe that Fig. 2.6(b) produces higher PSNR and lower MSRE because

visible but small distortion is added at the lower right corner of the image while

lower PSNR is achieved for image in Fig. 2.6(c) and (d) when the watermark is

embedded invisibly over the entire image in a spread spectrum fashion. Further, as

the higher embedding strengths produce more distortions, the behavior of

distortion measures is consistent and marking in Fig. 2.6(c) provides better quality

of images compared to that in Fig. 2.6(d). Furthermore, it is also argued that the

pixel-based metrics are applied directly to the luminance and chrominance of

images, so these metrics are unsuitable if two images are defined in different

colours spaces.

(a)

Original gray

scale image size

256256 pixels

(b)

Visible marking

000381.0

dBPSNR 77.39

(c)

Invisible marking

1.0001215.0

dBPSNR 73.34

(d)

Invisible marking

2.0002423.0

dBPSNR 72.31

Fig. 2.6. Examples of pixel-based imperceptibility measures.

35

We favour these metrics because of many reasons. First, our research

mainly focuses on the design and development of robust and fast watermarking

scheme in which the watermark will be embedded invisibly in the spread spectrum

fashion. Thus, it is ensured that the model and sources of distortion added will be

similar for all the algorithms under investigation. Second, all our test images are

8-bit gray-scale images and these metrics do not pose any problem if images are

defined in same colour space [25]. Finally, the absence of any other perfect metric

as well as the simplicity of the pixel-based metrics makes them an obvious choice

for evaluating the quality of watermarked images.

2.5.2. Watermark Robustness Metrics

The term robustness describes the watermark resistance to various attacks. The

evaluation method to measure robustness depends on the response of watermark

detector. There are three possible types of detector responses. First, hard decision

detector response that generates the binary output ‘true’, if the watermark is

present and ‘false’ otherwise. Second, soft decision detection that provides as

output the test statistic itself which is usually a real number related to detection

reliability such as correlation coefficient or similarity coefficient. The binary

decision can be reached by comparing the test statistic with certain threshold. The

robustness of hard and soft decision detectors can be measured using receiver

operating characteristic (ROC) graphs proposed by Kutter and Petitcolas [25].

Third, if the embedded watermark is in the form of a message, then the watermark

robustness can be evaluated directly using bit error rate )(BER which represents

the average number of bits extracted incorrectly [92]. Since our watermark

consists of pseudo-random sequence of bits, as discussed in Section 2.1, we use

BER to measure the watermark robustness under various attacks. However, a

brief overview of ROC graphs is presented for the sake of completeness of

discussion.

i. Bit Error Rate )(BER : It is defined as the ratio of number of bits extracted

inaccurately to total number of bits embedded. The robustness of the

watermarking system that embeds the binary watermark and whose

detector response is the bit sequence obtained after extraction, can be

36

measured quantitatively using BER . Many researchers [71,74] compute

BER as a function of image dimensions and inaccurately measure the

reliability of watermark extracted as follows.

NM

lyinaccurateextractedbitsofNumberBER

(2.3)

However, we find the above computation formula inaccurate as the

size of image in the denominator gives minuscule values to BER for large

image sizes. Typically, an image of 256256 pixels, which is quite

common in practical applications, will give 00153.0BER even if all the

100 bits are extracted inaccurately. Further, for evaluating watermark

robustness against scaling, it will be inappropriate to use Eq. (2.3) to

compute BER , as higher scaling factors and large image sizes will always

produce smaller values of BER . Thus, we compute BER irrespective of

the host image size as follows.

embeddedbitsofnumberTotal

lyinaccurateextractedbitsofNumberBER (2.4)

Using Eq. (2.4), the value of BER lies between 0 and 1 with

values of 25.0BER signifying that more than one-fourth of the bits

extracted are incorrect and presumably there is failure to extract the

watermark.

ii. ROC Graphs: A decision detector generates the binary response by

making a decision between the alternative hypothesis (the image is

watermarked) and the null hypothesis (the image is not watermarked).

Two common types of errors that can be produced by the detector are:

Type I error: It is also known as false positive and occurs when the

alternative hypothesis is accepted while the null hypothesis is correct.

Type II error: It is also known as false negative and occurs when the null

hypothesis is accepted while the alternative hypothesis is correct.

37

The robustness of a watermarking scheme can be computed

through ROC graphs by plotting the pairs of true positive fraction )(TPF

along y-axis and false positive fraction )(FPF along x-axis against the

varying threshold levels or decision criteria, where TPF and FPF are

computed as follows.

results test negative false of No.results test positive trueof No.

results test positive trueof No.

TPF

(2.5)

testsnegative trueof No.results test positive false of No.

results test positive false of No.

FPF (2.6)

The area under ROC curve measures the performance of the

detector. An optimal detector curve goes from the bottom left corner to

the top left corner and then towards the top right corner while that of the

random detector lies along the diagonals showing random selection of

both hypotheses with equal probability. The examples of optimal and

random ROC curves are shown in Fig. 2.7. To generate the ROC curve

depicting true behavior of detector, the set of test images should include

equal number of watermarked and non-watermarked images.

Fig. 2.7. Example ROC graph: Curves showing an optimal and random behavior

of a detector response against different thresholds.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

TPF

FPF

An optimal ROC curve

Random detection behaviour

38

2.5.3. Measuring Embedding Capacity

Embedding capacity refers to the maximum amount of information that can be

embedded. A major challenge while measuring the embedding capacity is the

quantification of “information”, which may be in the form of numeric values,

character strings, encrypted messages, logo images or simply a binary sequence.

For simplicity, it is assumed that all forms of information need to be converted to

binary form and storage size or number of bits can be used to quantify the size of

watermark signal. In general, the embedding capacity of watermarking system can

be measured either by using an absolute or a relative metric explained as follows.

i. Absolute Metrics: Absolute metrics measure capacity in terms of

maximum number of bits that can be embedded using a watermarking

scheme. For example, in case of spatial domain watermarking method that

uses each pixel position to embed a separate bit, the maximum embedding

capacity equals to the size of the host image. On the other hand, for a

transform domain watermarking scheme, the embedding capacity depends

on the number of magnitude independent transform coefficients that can

be used for embedding. Absolute metrics are not very reliable because

number of bits embedded is generally a function of host media size.

However, while analysing different watermarking schemes using same

host media sizes, absolute metrics are useful for a quick comparison.

ii. Relative Metrics: Lan and Tewfik [83] propose the use of hiding ratio or

the embedding ratio to measure the embedding capacity which is more

reliable. Hiding ratio is defined as the ratio between the embedding data

size and the original uncompressed host-media size. Another useful

relative measure of capacity is the compressed hiding ratio which is

defined as the ratio between the embedding data size and the compressed

host-media size. A high-capacity embedding must have hiding ratio above

0.5% and the compressed hiding ratio not less than 4%.

We use both absolute and relative measures for evaluating the embedding

capacity of the proposed watermarking scheme as well as comparing it with the

existing watermarking schemes.

39

2.5.4. Measuring Computational Cost

Computational cost mainly refers to the time complexity of the watermark

embedding and detection procedures. Theoretically, the computational cost should

be measured in terms of the exact number of operations required. But this method

is not an indicator of the actual time taken for the execution on a particular

configuration and can be misleading. Many times, fast methods provide better

speed by splitting a single complex operation into a number of simpler operations

to reduce execution time. Hence, an easier and better method to measure time

complexity is by recording CPU elapse time in a minimally configured hardware

and software suite. Further, the watermarking application should require small

hard disk space when installed as software.

2.6. Automated Benchmarking Tools

The performance evaluation of watermarking scheme must be carried out in a

consistent manner using sophisticated benchmarking methods in order to ensure

that the methods and algorithms proposed are strong and robust enough to

guarantee its success. Recently, many benchmarking tools have been proposed for

the performance evaluation of watermarking system which are freely available for

educational and research purposes.

Stirmark [93-95] is the first benchmarking software that was developed to

involve a variety of attacks including sharpening, GIF and JPEG compression,

scaling, cropping, shearing, rotation, column and line removal, flipping and the

‘Stirmark’ attack which is a combination of slight geometric and intensity

distortions. The software takes two inputs, a set of watermarked images and user

defined binary decision detector in the executable form and applies a number of

attacks (one at a time) in every watermarked image before calling the detection

routine. It provides a method for defining the number and parameters for attacks

through configuration files but does not support automatic execution of trials

involving different keys or messages. The software also lacks features like

evaluation of time complexity and graphical user interface. The responses of the

benchmark need to be compiled and analysed through separate data management

tools like Excel. Despite these limitations, Stirmark is widely used by researchers

40

because of its ability to act as an attack-machine, ease of use, support for

multimedia digital contents and endorsement for primitive evaluation of

performance statistics. We use Stirmark 4.0 which is available freely from its

official site http://www.petitcolas.net/fabien/watermarking/stirmark.

Some other available benchmarking platforms are Checkmark, Optimark,

Certimark, unsign, openwatermark and recently developed software watermark

evaluation testbed. Checkmark [96] can be considered as a successor of Stirmark.

Apart from regular Stirmark attacks, it incorporates new attacks like wavelet

compression (JPEG 2000), projective transformations, modeling of video

distortions, warping, copy attack, template removal attack, de-noising, nonlinear

line removal, collage attack, down/up sampling, dithering and thresholding and

also allows inclusion of user defined attacks. However, the basic operating

principles of Checkmark are very similar to those of Stirmark, therefore, it suffers

from inherit limitations such as no option for automatic execution of multiple

trials, no evaluation of the false alarm probability, failure to address watermark

detection and message decoding separately and no complexity evaluation.

Checkmark is available as Matlab open source.

Solachidis et al. [97] proposed a new benchmark Optimark that

incorporates the same attacks as Stirmark but with graphical user interface. In

addition to two standard inputs, Optimark requires an embedding executable to

support a range of keys and messages that can be used while testing. The tool also

allows cascades of attacks and can be used for both hard and soft decision

detectors. Raw results can be automatically processed to provide a number of

performance metrics and plots in HTML format including mean embedding and

detection time, ROC graphs, Bit Error Rate and payload for the watermarking

scheme that allows message encoding. The main drawbacks of Optimark are the

lack of expandability with respect to attacks and the use of simple perceptual

quality metric.

Certimark [98] is a benchmarking platform using a client-server, web-

based structure with open architecture that allows easy integration of new

functionalities. Its major characteristics are flexible interface to plug-in watermark

embedding and detection software, separate control file in the XML format for

41

describing the watermarking parameters, specific module for writing benchmark

report with tables and graphics to ease analysis and an additional “Result and

Certificate” module that compares the actual results with performance

specification criteria to generate a certificate of compliance.

Two other open-source web-based benchmarking systems are watermark

evaluations testbed (WET) [99] and OpenWatermark [100]. The WET consists

of three major components: the Front End, the Algorithm Modules and the Image

Database. The Front End is the end users' web-based interface into WET and

consists of a web server, database server and the GIMP-Perl server. It provides an

interface whereby a user can select images to be watermarked, embedding and

detection processes and attacks to be applied. The algorithms modules provide

tools that can be used standalone in specific test environments allowing users to

validate their tests locally before submitting them to a watermark benchmark site

and the image database provide nearly thousands of copyright free images using

MySQL as the database engine. OpenWatermark is based on Java technology and

provides platform independence. It is easily portable on any platform supporting

Java, RMI and JDBC. The OpenWatermark platform supports Windows and

Linux executables (written in C or C++), as well as Matlab and Python scripts.

In addition to the above mentioned evaluation tools, we also surveyed

some image watermarking software tools available as freeware. A brief

description of these tools is given in Appendix A.

2.7. Application Areas of Digital Watermarking

Digital watermarking can be used for a variety of applications. Following are

some of the broad application areas in which digital watermarking can be put into

practice.

2.7.1. Owner Identification and Copyrights Protection

One of the major application areas of digital watermarking is the protection of

copyrights through owner identification and assertion to settle the dispute

regarding proof of ownership and royalties. Robust watermarking is used to

42

embed the information about the original owner or creator of the digital contents

because it can survive common signal processing and intentional attacks to

remove the watermark. Although the legal status of the watermarking in the court

of law is yet to be established, the use of digital watermarking is more

advantageous than the use of visible tags or file header with copyright

information. A robust watermark cannot be removed without causing severe

degradation to the quality of watermarked digital contents. Further, no extra space

is required to store the watermark and the size of original digital content remains

the same. Hence, digital watermarking is considered more suitable for the digital

rights management.

2.7.2. Content Authentication, Integrity Verification and Tamper Detection

For the digital contents pertaining to artworks, legal documentation, medical

records, commercial transactions, identity proofs, photographs for court evidence,

it is extremely important to ensure that the contents originated from a specific

source are authentic. For such a purpose, a fragile watermark is embedded at the

source that can verify the integrity of the image and report a failure if the contents

are forged. However, in certain scenarios, the digital contents may have

undergone format conversion and/or compression (e.g. uncompressed AVI to

MP4) for compatibility or reduction in size for storage. In such cases, the

formatted contents must be authenticated by the watermarking system. A

watermark system that can distinguish between the unintentional signal processing

attacks and malicious attacks and authenticate the digital contents accordingly is

called a semi-fragile watermarking system and is an emerging field of research.

Further, digital watermarking can also be used to reveal the alterations

made in the digital contents. Content authentication and tamper detection are

closely related. If a media is detected to be tampered, this means that it is not

authenticated and unauthenticated contents are bound to be tampered. Tamper

detection techniques are based on the concepts of localisation to identify the key

regions where alterations have been made. This information can be used by media

forensics to find the motives behind tampering.

43

2.7.3. Transaction Tracking and Piracy Control

In order to prevent the unauthorised or illegal distribution of the copyright digital

contents, a watermarking application may embed unique label, known as digital

fingerprint, prior to the legal distribution of contents. At a later stage, if

unauthorised copies of the contents are found, then the origin of the piracy can be

determined by retrieving the fingerprint embedded in the image. Such activity of

transaction tracking is referred to as fingerprinting. A digital fingerprint is

distinguished from a digital watermark on the basis that the former contains

significant information about an authorised recipient while the latter about the

original creator or owner. One of the major challenges associated with

fingerprinting is the collusion attack in which a number of authorised recipients

collide and create an un-watermarked version of the digital contents from the

watermarked versions for illegal distribution. In order to solve this problem, many

collusion-resistant fingerprinting techniques have been designed and developed by

various researchers [101]. DiVX Corporation deployed a transaction tracking

system. Each DiVX-enabled DVD player embedded a unique watermark into the

video that it played. If the video is subsequently pirated and redistributed, the

DiVX Corporation could use the watermark to identify the exact player used and

thereby identify the source of the pirated work [13].

2.7.4. Copy Control

Watermarking can also be used for prevention of illegal copying of digital

contents. An informative watermark can be embedded indicating the number of

copies that are permitted. Such a watermarking system may require special

hardware/software that manipulates the watermark each time a copy is created and

prevent further copy operation once the limit is crossed.

Today many manufacturers use playback control technology to prevent

illegal copying of contents. For example, PlayStations and Portable Play Stations

(PSP) by Sony are the compliant devices that can check for watermarks in the

content being played. When the device sees never-copy watermark, it checks to

determine if it is playing a copy or original. If the contents are copied, the player

stops playing the game. One technical issue related to the use of watermarking for

44

copy control is the requirement that watermark must be detectable and modifiable

by everyone. This may result in weak watermark security.

2.7.5. Broadcast Monitoring

In a famous case associated with overbooking of air time by a broadcasting station

[102], advertisers were paying for thousands of commercials that were never

aired. In order to avoid such scams, watermarking can be used for active

monitoring to ensure that the commercials are broadcasted at the times and

locations of their agreements with broadcasters. Broadcast monitoring is used for

the prevention of illegal distribution of digital contents, determination of royalty

payments and ensuring advertisers that their commercials are being broadcast at

times and locations they have purchased. A digital content owner may watermark

his contents by embedding a unique watermark which is detected by an automated

monitoring system that monitors the broadcasts to keep track of when and where

the content appears. This will ensure that their contents are not illegally

distributed and help owners in determining royalty payments which is extremely

important for commercial advertisers as they actually pay for only the number of

times the advertisement was actually relayed.

Several companies provide watermarked-based broadcast monitoring

services. For example, Teletrax offers a service that is supported by on-video

watermarking technology from Philips. Some other examples of video monitoring

technologies are VEIL-II and MediaTrax.

2.7.6. Annotation and Privacy Control

Digital watermarking can also be used for embedding the annotations for

automatic information retrieval and electronic document indexing. An annotation

is a watermark that contains information about the digital contents in the form of

indices and keywords. For example, patient information or enrollment number can

be annotated on MRI scans or X-rays to avoid mixing of reports between two

patients. Both visible and invisible watermarking can be used for this purpose.

While the patient identity number can be embedded using visible watermarking,

the sensitive information such as name, age, gender etc. must be embedded

45

invisibly in order to maintain the privacy. Watermark robustness is required as

annotations must survive common signal processing attacks and geometric

distortions.

2.7.7. Persistent Item Identification

Digital watermarking can be used to embed an identifier in the digital contents to

prevent certain content alterations. This identifier can be linked with the database

containing further information about the digital contents such as usage control,

copyright information and host data enhancement features like access to free

services and products with the genuine purchase of the digital contents in such a

way that the enhancement features are disabled if the contents are distributed

illegally or the watermark is tampered. This will implicitly control the evils of

piracy and forgery in the digital multimedia environment.

2.7.8. Legacy System Enhancement

Sometimes a system needs an upgrade to improve the functionality, which may be

incompatible with the existing system. For example, during recent transition from

analog to digital television in many advanced nations like United States, Japan

and United Kingdom and even in the metropolitan cities of India, new digital

broadcasting equipment has to be deployed and the consumers must purchase

digital television receivers. Meanwhile, the legacy analog system must continue to

work until the transition is complete. Since it is a costly and time-consuming

process, it is essential that the new upgrade system must have backward

compatibility until analog television broadcast actually ceases to exist.

Digital watermarking can be used to enhance the functionality of legacy

system. One example is Tektronix’s digital watermark encoder [103] that can

synchronise the audio and video signals being processed separately by a television

system. During analog to digital transition, different delays are introduced in the

audio and video channels resulting in poor lip-synchronisation. The Tektronix

product embeds a highly compressed audio signal as a watermark within the video

signal. After digital processing, the real time audio signal is compared with the

embedded signal in order to adjust the time delays before broadcasting.

46

2.8. Conclusions

Digital watermarking is a vast field that involves watermark embedding in

multimedia contents and detection for many different purposes. This chapter

provides a general overview of the concepts, applications and challenges related to

digital watermarking, while desirable properties, classification methods,

evaluation tools and techniques are discussed specifically to image watermarking.

The chapter begins with the generic model of digital watermarking and

defines basic terminology to be used throughout the thesis. We also describe some

desirable properties of an efficient image watermarking. While some of the

properties like robustness, embedding capacity and decoder performance are

specific to applications for which a watermarking system is required, others like

imperceptibility, computational cost, security etc. are general properties desirable

by all watermarking systems. Further, depending on the properties required and

different application scenarios, we have classified an image watermarking system

into various categories.

The efficiency of a robust watermarking system lies in its capability to

resist attacks. We have presented a detailed survey on various possible attacks on

watermarked images. We differentiate among un-intentional attacks, common

signal processing attacks and threats due to malicious attacks. The design of

robust watermarking schemes mainly concerns un-intentional and common signal

processing attacks including image enhancement, image degradation and

geometric attacks while security against threats like forgery and watermark

removal require research on allied fields like encryption, security and copyright

infringement laws and enforcement.

Further, unless designs or system implementation are not evaluated

quantitatively and systematically using some standard automated benchmarks, the

claims about efficiency of the proposed system and its improvement over existing

techniques cannot be persuaded. Thus, we incorporated detailed discussion on the

evaluation of watermarking systems, technical issues involved and various

benchmarks available with their relative advantages and disadvantages. The

chapter concludes with lighter notes describing application areas of digital

47

watermarking in the real world scenarios. Wherever possible, we have tried to

quote practical examples throughout the chapter.

Some original works presented in this chapter include broader taxonomy

of watermarking types and classification of attacks as shown in Figs. 2.2 and 2.3.,

respectively. Our research is focused on the robust, imperceptible and informative

kind of watermarking using moment invariants with a blind detector. Further,

watermark security to deal with the threats or malicious attacks will remain out of

the scope of this research work. Other contributions are establishing significance

of pixel-based difference distortion measures to evaluate visual imperceptibility

and a novel attempt to define absolute and relative metrics for measuring

embedding capacity.

This chapter concludes the general discussion on digital watermarking.

Detecting the watermark in the presence of common geometric distortions and

their combinations is still a challenge in robust watermarking. Computational cost

is another crucial requirement and needs to be minimised for practical

implementations. Next chapters will focus on the state-of-art of robust

watermarking techniques and design and development of algorithms to enhance

the robustness and speed of watermarking.

48