project4

“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________

Chapter 1

INTRODUCTION

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Dept of IT,JSSATE, Bangalore 8th Semester Project 2010


INTRODUCTION

Information is becoming widely available via global networks. These

connected networks allow cross-references between databases. The advent of

multimedia is allowing different applications to mix sound, images, and video

and to interact with large amounts of information (e.g., in e-business,

distance education, and human-machine interface). The industry is investing

to deliver audio, image and video data in electronic form to customers, and

broadcast television companies, major corporations and photo archivers are

converting their content from analog to digital form.

An important factor that slows down the growth of multimedia

networked services is that authors, publishers and providers of multimedia

data are reluctant to allow the distribution of their documents in a networked

environment. This is because the ease of reproducing digital data in their

exact original form is likely to encourage copyright violation, data

misappropriation and abuse. Replicas of a given piece of digital data cannot

be distinguished and their origin cannot be confirmed. These are the

problems of theft and distribution of intellectual property. Therefore, creators

and distributors of digital data are actively seeking reliable solutions to the

problems associated with copyright protection of multimedia data.

Moreover, the future development of networked multimedia systems, in

particular on open networks like the Internet, is conditioned by the

development of efficient methods to protect data owners against unauthorized

copying and redistribution of the material put on the network. This will

guarantee that their rights are protected and their assets properly managed.

Copyright protection of multimedia data has been accomplished by means of

cryptography algorithms to provide control over data access and to make data

unreadable to non-authorized users. However, encryption systems do not

completely solve the problem; because once encryption is removed there is no

more control on the dissemination of data.

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The concept of digital watermarking arose while trying to solve

problems related to the copyright of intellectual property in digital media. It

is used as a means to identify the owner or distributor of digital data.

Watermarking is the process of encoding hidden copyright information since

it is possible today to hide information messages within digital audio, video,

images and texts, by taking into account the limitations of the human audio

and visual systems.

1.1 Motivation

Digital watermarking is a form of data hiding or Steganography.

Digital watermarking is intended as the solution to the need to provide value-

added protection on top of data encryption and scrambling for content

protection. Motivated by growing concern about the protection of intellectual

property on the Internet and limitations of encryption techniques, the interest

of watermarking techniques has been increasing over the recent years.

1.2 Definition

Digital image watermarking is a technique which allows an individual

to add hidden copyright notices or other verification messages to digital

images [1].without destroying the quality or information content of the

image. Such messages contain information pertaining to the signal or to the

author of the signal (name, place, etc.). The technique takes its name from

watermarking of paper or money as a security measure. The watermark is

hidden in the host data in such a way that it is inseparable from the data and

so that it is resistant to many operations (called attacks). Thus by means of

watermarking, the work is still accessible but permanently marked.

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1.3 Literature

Digital watermarking techniques derive from Steganography, which

means covered writing (from the Greek words stegano or “covered” and

graphos or “to write”) [3]. Steganography is the science of communicating

information while hiding the existence of the communication. The goal of

Steganography is to hide an information message inside harmless messages in

such a way that it is not possible even to detect that there is a secret message

present.

Thus both Steganography and watermarking belong to a category of

information hiding, but the objectives and conditions for the two techniques

are just the opposite. In watermarking, for example, the important

information is the “external” data (e.g., images, voices, etc.). The “internal”

data (e.g., watermark) are additional data for protecting the external data and

to prove ownership. In Steganography, however, the external data (referred to

as a vessel, container, or dummy data) are not very important. They are just a

carrier of the important information. The internal data are the most important.

Watermarking cannot be compared to encryption although they were

both designed keeping the same thing in mind i.e. protection of data from

piracy. Watermarking does not restrict access to the data while encryption

has the aim of making messages unintelligible to any unauthorized persons

who might intercept them. Once encrypted data is decrypted, the media is no

longer protected. A watermark is designed to permanently reside in the host

data. If the ownership of a digital work is in question, the information can be

extracted to completely characterize the owner.

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“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________1.3.1 Description

Digital image watermarking is a technique which allows an individual

to add hidden copyright notices or other verification messages to digital

images.without destroying the quality or information content of the image.

Such messages contain information pertaining to the signal or to the author of

the signal (name, place, etc.). The technique takes its name from

watermarking of paper or money as a security measure. The watermark is

hidden in the host data in such a way that it is inseparable from the data and

so that it is resistant to many operations (called attacks). Thus by means of

watermarking, the work is still accessible but permanently marked.

Digital watermarking offers several advantages. The details of a good

digital watermarking algorithm can be made public knowledge. Digital

watermarking provides the owner of a piece of digital data the means to mark

the data invisibly. The mark could be used to serialize a piece of data as it is

sold or used as a method to mark a valuable image. For example, this marking

allows an owner to safely post an image for viewing but legally provides an

embedded copyright to prohibit others from posting the same image [3].

The contents of the image can be marked without visible loss of value

or dependence on specific formats. For example a bitmap (BMP) image can be

compressed to a JPEG image [2]. The result is an image that requires less

storage space but cannot be distinguished from the original. Generally, a

JPEG compression level of 70% can be applied without humanly visible

degradation. This property of digital images allows insertion of additional

data in the image without altering the value of the image. The message is

hidden in unused “visual space” in the image and stays below the human

visible threshold for the image.

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“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________1.3.2 Building an Effective Watermarking Algorithm

A simple block diagram of a watermarking algorithm is shown in Fig

1.1. As the diagram shows the watermarking algorithm combines the original

image and the watermark image to give the watermarked image.

Fig 1.1: Block diagram of a watermarking algorithm

In case detection is necessary, then Fig 1.2 shows the block diagram of

a detection algorithm. The detector algorithm generally follows a reverse path

to that of the watermarking algorithm.

Fig 1.2: Block diagram of a watermark detection algorithm

The dotted line implies the original image may or may not be used for

detection. When it is used we call it as private watermarking and if it is not

used then we call it a public watermarking scheme.

Some of the properties desirable in a watermark are discussed below.

These should be kept in mind when designing a watermarking system.

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Original image

Watermarking Algorithm

Watermarked image

Watermark

Watermarked image

Detector Algorithm

Watermark

Original image or watermark


A watermark shall convey as much information as possible, which

means the watermark data rate should be high.

A watermark is said to have high fidelity, if the degradation it causes is

very difficult for a viewer to perceive [3]. However, it only needs to be

imperceptible at the time that the media is viewed. If we can be certain that

the media will be seriously degraded before it is viewed, we can rely on that

degradation to help mask the watermark.

It is desired that watermarks survive image-processing manipulations

such as rotation, scaling, image compression and image enhancement, for

example. Robustness against geometrical transformation is essential since

image-publishing applications often apply some kind of geometrical

transformations to the image, and thus, an intellectual property ownership

protection system should not be affected by these changes [7].

Watermarked images are often subjected to attacks in order to remove

the copyright protection. Tamper resistance refers to a watermarking system’s

resistance to hostile attacks. Watermarks and attacks on watermarks are two

sides of the same coin. The goal of both is to preserve the value of the digital

data. However, the goal of a watermark is to be robust enough to resist attack

but not at the expense of altering the value of the data being protected. On

the other hand, the goal of the attack is to remove the watermark without

destroying the value of the protected data.

In practice, it is probably impossible to design a watermarking system

that excels at all of these. Thus, it is necessary to make tradeoffs between

them, and those tradeoffs must be chosen with careful analysis of the

application.

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“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________1.3.3 Classifications of digital image watermarking

Some of the most general classifications of image watermarks are

discussed here [7] .

Visible and Invisible Watermarks

Digital watermarking can be divided into two main categories: visible

and invisible. The idea behind the visible watermark is very simple. The

watermark is purposefully made perceptible. It is equivalent to stamping a

watermark on paper, and for this reason is sometimes said to be digitally

stamped. One example is the visible watermarking of preview images

available in image databases or on the World Wide Web in order to prevent

people from commercial use of such images. Invisible watermarking, on the

other hand, is a far more complex concept. It is most often used to identify

copyright data, like author, distributor, and so forth.

Visible and invisible watermarks both serve to deter theft but they do

so in very different ways. Visible watermarks are especially useful for

conveying an immediate claim of ownership. The main advantage of visible

watermarks, in principle at least, is that they virtually eliminate the

commercial value of the document to a would-be thief without lessening the

document's utility for legitimate, authorized purposes. Invisible watermarks,

on the other hand, are more of an aid in catching the thief than discouraging

the theft in the first place.

Spatial domain and Transform domain watermarks

The watermark may be inserted into the image either in the spatial

domain or the transform (frequency) domain. Spatial domain techniques are

easier to implement. However with transform domain techniques it is easier

to develop invisible watermarks and also the watermarks produced are more

robust to attacks.

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The idea is to directly insert a watermark in the pixel value when the

image is in the spatial domain. The robustness of this domain is weak

compared to transform domain. The most widely used algorithm in the spatial

domain is – Least Signification Bit (LSB) technique, which is the simplest

method of inserting the watermark. As the LSB provides the least

information in a byte, it can be replaced with the watermark bit. Hence the no

of watermark bits that can be inserted depends on the size of the image.

In the transform domain, we transform the spatial image into the

frequency domain and insert the watermark information by changing the

frequency coefficient. The frequency domain can overcome the greatest

disadvantage of techniques operating in the spatial domain. The frequency

domain watermark is less susceptible compared with the spatial domain, the

LSB technique can also be applied in the frequency domain. The watermark

normally applies to the lower frequencies or mid frequencies within an image,

as higher frequencies are usually lost when an image is compressed.

Frequency-based techniques result in a watermark that is dispersed

throughout the image and are less susceptible to attack by cropping. In

majority of the images, the values of high frequency coefficients are small.

The human vision system is not sensitive to high frequency. Therefore, we

always adopt the thick quantification to high frequency coefficients. Low

energy in high frequency coefficients does not affect the degradation of

image quality of reconstructed image.

Figs 1.3 below shows some of the methods used in the spatial and

transform domains for image watermarking.

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Fig 1.3: Classification of watermarking based on embedding domain

As shown in the above figure we can further classify spatial domain

techniques into modification of LSB and spread spectrum techniques. The

LSB bits contain the least information and hence altering them to represent

the watermark will not change the original image by much. Spread spectrum

techniques involve both direct sequence and frequency hopping methods. The

methods in transform domain are identified by the transforms they use like

discrete cosine transforms, wavelet transforms etc [7].

1.3.4 Discrete cosine transforms

One of the most commonly used transforms in image processing is the

Discrete Cosine Transform (DCT) [4]. Since we make use of DCT in

implementing both the algorithms, a brief description of DCT is given in this

section.

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Watermarking Embedding Domain

Spatial Domain Transform Domain

Wavelets transform (DWT)

Cosines transform (DCT)

Spread Spectrum

Modification of LeastSignificant Bit (LSB)

Other transforms


The One-Dimensional DCT

The most common DCT definition C(u) of a function f(x) which is a 1-

D sequence of length N is

……………(1)

for u= 0,1,2,…,N− 1.

where the function α(u) is defined as

…………....(2)

Similarly, the inverse transformation is defined as

… … … … … … … … … … … …(3)

for x= 0,1,2,…,N− 1 and the

function α(u) is defined as in equation (2)

It is clear from (1) that for:

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Thus, the first transform coefficient is the average value of the sample

sequence. In literature, this value is referred to as the DC Coefficient . All

other transform coefficients are called the AC Coefficients .

The Two-Dimensional DCT

The 2-D DCT [4] is a direct extension of the 1-D case. The 2-D DCT

definition C(u,v) of a function f(x,y) which is a 2-D sequence of size NxN is

……….... .(4)

for u ,v = 0,1,2,…,N −1.

where α (u ) and α (v ) are as defined in equation (2).

The inverse transform is defined as

……………(5)

for x, y = 0,1,2,…,N −1.

where α (u) and α (v) are as defined in equation (2).

The 2-D basis functions can be generated by multiplying the

horizontally oriented 1-D basis functions with vertically oriented set of the

same functions. Again, it can be noted that the basis functions exhibit a

progressive increase in frequency both in the vertical and horizontal

direction. The top left basis function results from multiplication of the DC

component with its transpose. Hence, this function assumes a constant value

and is referred to as the DC coefficient.

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“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________Properties of DCT

Here we discuss some of the properties of DCT that make it highly

useful in image watermarking [4].

1. Decorrelation

The principle advantage of image transformation using DCT is the

removal of redundancy between neighboring pixels. This leads to

uncorrelated transform coefficients which can be encoded independently. The

amplitude of the autocorrelation after the DCT operation is very small at all

lags. Hence, it can be inferred that DCT exhibits excellent decorrelation

properties.

2. Energy Compaction

Efficacy of a transformation scheme can be directly gauged by its

ability to pack input data into as few coefficients as possible. This allows the

quantizer to discard coefficients with relatively small amplitudes without

introducing visual distortion in the reconstructed image. DCT exhibits

excellent energy compaction for highly correlated images. The uncorrelated

image has more sharp intensity variations than the correlated image.

Therefore, the former has more high frequency content than the latter. The

uncorrelated image has its energy spread out, whereas the energy of the

correlated image is packed into the low frequency region (i.e., top left

region).

Advantages of DCT

Compared to other transforms DCT, it has fixed basis images and fast

implementations are possible. It also exhibits good decorrelation and energy

compaction characteristics [5]. However, a transform like DFT is a complex

transform and therefore stipulates that both image magnitude and phase

information be encoded. Furthermore, the implicit periodicity of DFT gives

rise to boundary discontinuities that result in significant high-frequency ________________________________________________________________________


“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________content. After quantization, Gibbs Phenomenon causes the boundary points to

take on erroneous values, which appears in the image as blocking artifacts.

That is the boundaries between the adjacent sub images become visible

because the boundary pixels of the adjacent sub images assume the mean

value of discontinuities formed at the boundary points. The DCT reduces this

effect, because its implicit 2n point periodicity does not inherently produce

boundary discontinuities.

Sub image size selection

Another important factor affecting the transform coding and

computational complexity is the sub image size [14]. In watermarking, image

is divided so that the correlation (redundancy) between adjacent sub images

is reduced to some acceptable level and so that n is the dimension of the sub-

image which is an integer power of 2, n usually takes values of 2,4,8,16 or

32. But with 8x8 as the sub image size the root mean square error is least it is

usually preferred in DCT algorithms used in image processing.

1.4 Brief results

We have successfully implemented both visible and invisible

watermarking using Discrete Cosine Transform. The implementation is

depicted in results.

In visible watermarking the image of Lena is the cover image and our

college logo is the watermark. The logo is satisfactorily embedded in the host

image without any significant degradation. In case of colour image

watermarking the picture of the national emblem is embedded in picture of

Tajmahal which is the cover image.

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Invisible watermarking was implemented by embedding a watermark

image of size (1/64) t h the size of the cover image, that is maximum size of

watermark that can be embedded . Invisible watermarking is performed for

different values of scaling factors and hence different watermarked images

with different quality are obtained.

Chapter 2

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MATLAB

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MATLAB

MATLAB is a high-performance language for technical computing. It

integrates computation, visualization, and programming in an easy-to-use

environment where problems and solutions are expressed in familiar

mathematical notation. The name MATLAB stands for matrix laboratory.

Typical uses include Math and computation, Algorithm development,

Data acquisition Modeling, simulation, and prototyping Data analysis,

exploration, and visualization Scientific and engineering graphics

Application development, including graphical user interface building.

MATLAB is an interactive system whose basic data element is an array

that does not require dimensioning. This allows you to solve many technical

computing problems, especially those with matrix and vector formulations, in

a fraction of the time it would take to write a program in a scalar no

interactive language such as C or any other language.

MATLAB has evolved over a period of years with input from many

users. In university environments, it is the standard instructional tool for

introductory and advanced courses in mathematics, engineering, and science.

In industry, MATLAB is the tool of choice for high-productivity research,

development, and analysis.

MATLAB features a family of add-on application-specific solutions

called toolboxes. Very important to most users of MATLAB, toolboxes allow

you to learn and apply specialized technology. Toolboxes are comprehensive

collections of MATLAB functions (M-files) that extend the MATLAB

environment to solve particular classes of problems. Areas in which

toolboxes are available include signal processing, image processing, control

systems, neural networks, fuzzy logic, wavelets, simulation, and many others.

One such toolbox used extensively in this project is image processing

toolbox.

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2.1 What is the Image Processing Toolbox?

The Image Processing Toolbox is a collection of functions that extend

the capability of the MATLAB numeric computing environment. The toolbox

supports a wide range of image processing operations, including

Spatial image transformations

Morphological operations

Neighborhood and block operations

Linear filtering and filter design

Transforms

Image analysis and enhancement

Image registration

Deblurring

Region of interest operations

MATLAB 7.0 with inbuilt toolboxes was used in WINDOWS XP environment

to implement this project.

Chapter 3

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IMPLEMENTATION

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IMPLEMENTATION

In this chapter we discuss about the implementation details of our

project. Since we have used two methods, one each for visible and invisible

watermarking, we discuss both methods in sufficient detail.

3.1 Visible watermarking

In visible watermarking of images, a secondary image (the watermark)

is embedded in a primary (host) image such that watermark is intentionally

perceptible to a human observer whereas in the case of invisible

watermarking the embedded data is not perceptible, but may be detected by a

computer program [6]. The perception of the host as well as the watermark

can be controlled as per the application.

Some of the characteristics of visible watermarks are

1. A visible watermark should be visible in both color and monochrome

images.

2. The watermark should be spread in a large or important area of the

image in order to prevent its deletion by clipping.

3. The watermark must not significantly obscure the image details beneath

it .

4. The watermark must be difficult to remove; removing a watermark

should be more costly and labor intensive than purchasing the image

from the owner.

5. The watermark should be applied automatically with little human

intervention and labor.

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3.1.1 Algorithms and flow charts

The algorithm we have followed for visible watermarking is discussed

in this section. It is a transform domain approach that uses DCT.

The equation defining the insertion of watermark in the DCT domain is

as follows,

Xij(n) = αn Cij(n) + βn Wij(n).. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .(1)

where n = 0,1,2…N-1 and N is the total number of 8x8 blocks in the original

image, n represents the position of block in the original image I.

The α n and β n coefficients are for block n. The Cij(n) are the DCT

coefficients of the host image block I and Wij(n) the DCT coefficients of the

watermark image block W. The α n and β n values are found out using a

mathematical model developed by exploiting the texture sensitivity of the

human visual system (HVS). This ensures that the perceptual quality of the

image is better preserved. We call α n the scaling factor and β n as the

embedding factor. Xij(n) the DCT coefficients of watermarked image.

Finding the scaling and embedding factors

While finding the scaling factors (α n) and embedding factors (β n), the

following are taken into consideration so that the quality of the watermarked

image is not degraded.

The distortion visibility is low when the background has strong texture.

In a highly textured block, energy tends to be more evenly distributed among

the different AC DCT coefficients. That means AC DCT coefficients of

highly textured blocks have small variances and we can add more to those

blocks. So for convenience, we assume α n to be directly proportional to

variance (σ n) and β n to be inversely proportional to variance (σ n).

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Let us denote the mean gray value of each image block as μ n and that of

the image as μ. The blocks with mid-intensity values (μ n ≈ μ) are more

sensitive to noise than that of low intensity blocks (μ n < μ) as well as high

intensity blocks (μ n > μ). This means that α n should increase with μ n as long

as (μ n < μ) and should decrease with μ n as long as (μ n > μ).For convenience,

the relationship between α n and μn is taken to be truncated Gaussian. The

variation of β n with respect to μ n is the reverse of that of α n . The mean gray

value of each block is given by its DC DCT coefficient.

Using the observations made in the above discussions α n and β n are computed

as:

αn = σ’n exp. ( - (μ’n - μ’ )2 )...................................……………………(2)

βn = (1/ σ’n) (1 – exp. ( - (μ’n - μ’ )2 .......................................................(3)

where μ’ n , μ’ are the normalized values of μ n and μ respectively, and σ’ n is

the normalized logarithm of σ n (the variance of the AC DCT coefficients).

The α n and β n are then scaled to the ranges (α m i n , αm a x) and (βm i n , βm a x)

respectively, where α m i n and αm a x are the minimum and maximum values of the

scaling factor, and β m i n and βm a x are the minimum and maximum values of the

embedding factor. These are the parameters determining the extent of

watermark insertion.

Divide the original image I into 8x8 blocks and find the DCT

coefficients of each block. Let us denote the DCT coefficients of block n by

Cij(n),n = 1, 2 . . . N, where n represents the position of block in image I (if

we traverse the image in a raster-scan manner).

N = (row x col)/ 64........................................................................................(4)

where N is the total number of 8x8 blocks in the image. And "row" is the

number of rows and "col" is the number of columns of the image.

The normalized mean gray value of block n is given by,

μ’n = C00(n) / C 0 0 max..................................................................................................................................(5)________________________________________________________________________



where C 0 0(n) are the DC coefficients and C0 0 m a x is the maximum value of

C0 0(n).

The normalized mean gray value of the image I, is given by,

μ’ = (1/N) ΣN n=1 C00(n) / C 0 0 max ...............................................................(6)

The variance of the AC DCT coefficients (σ n) of block n is given by,

σn = (1/64) Σ i Σ j (Cij - μnA C)2 ......................................................................(7)

where μ nAC is the mean of the AC DCT coefficients, i = 0,1,2….N-1 and j=

0,1,2….N-1.

Note: While calculating variance we also include the DC component in order

to prevent the case of zero variance.

Let us denote the natural logarithm of σ n as σ* n .The normalized variance of

the AC DCT coefficients of block n is of the value given by,

σ’n . = σ* n . / σ*m a x ........................................................................................................................................................................(8)

where σ*m a x is the maximum value of σ* n .

The typical values of α m i n , αm a x ,βm i n , βm a x ( especially when the

watermarked images are to be viewed through the internet) are around 0.95,

0.98, 0.07 and 0.17 respectively.

The plots for the above expressions are given in Fig.3.1-Fig.3.4.

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Fig 3.1: variation of α n with σ n

As we see in the above figure the scaling factor α n increases linearly

with variance σ n ranging from a low of 0.95 to a high of 0.98.

Fig 3.2: variation of β n with σ n

As we see in the above figure the embedding factor β n decreases

exponentially with variance σ n starting from around 0.17 for small variances

and decreasing to 0.07 for high variances.

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Fig 3.3: variation of α n with μ n

As we see in the above figure the scaling factor α n increases from a

value of 0.955 at low values of mean gray level to a maximum of 0.98 at mid

values of mean gray level and then decreasing back to 0.955 at higher values

of mean gray level.

Fig 3.4: variation of β n with μ n

As we see in the above figure the embedding factor β n decreases from a

value of 0.17 at low values of mean gray level to a minimum of 0.07 at mid

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“Digital image watermarking using Discrete Cosine Transform” ________________________________________________________________________values of mean gray level and then increasing back to 0.17 at higher values of

mean gray level.

Thus from the four figures we can see that the values of scaling and

embedding factors conform to the ranges that were discussed above.

Fig 3.5 gives the schematic representation of the insertion process.

Fig 3.5: Watermark insertion process

The original image is divided into blocks of size 8x8 each. The DCT of

each block is taken. We analyze the properties of these blocks to find the

scaling and embedding factors. The watermark image is similarly divided into

8x8 blocks and DCT of each block is taken. Finally we add the corresponding

elements of the original image times the scaling factor and the watermark

image times the embedding factor to obtain the watermarked image.

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Flowchart

The original image IM and the watermark image WM are divided into blocks of size

8x8. Depending on the properties of IM, the scaling and embedding factors are found using

the above mentioned algorithm. Finally we add the corresponding elements of the

original image times the scaling factor and the watermark image times the

embedding factor to obtain the watermarked image XM. This is shown in Fig

3.6.

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Start

The original image IM and the watermark image WM are divided into blocks of size 8x8.

N=size (IM)/8*8

n=1

Let Wij(n) be 8x8 blockwise DCT of the watermark image, n representing the block

Is

n<=N

No

Yes

L1

n=n+1


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n=n+1

Find the variance of each 8x8 block. Let this be blk_acvar(n). Then find the maximum among these as maxvar.

n=1, sumdc=0

Let Cij(n) be the 8x8 blockwise DCT of the original image

Is

n<=N

No

Yes

L2

Sumdc=sumdc+ C00(n)

Let maxdc be the maximum DC component among all the 8x8 blocks

Find the average of all values in each 8x8 block. Let this be blk_meanac(n)

L1


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temp(n)=exp(-((blk_meandc(n)-im_meandc)^2) )

Find the normalized variance of each 8x8 block :-blkn_acvar(n) = log(blkn_acvar(n))

log(maxvar)

Find the normalized mean dc of the entire image

blk_meandc(n)=C00(n)/maxdc

L2

Find the normalized mean dc of the entire imageIm_meandc=sumdc / (N * maxdc)

n=1

Is

n<=N

Yes

L3

No

scalef(n)=blkn_acvar(n)*temp(n)

embedf(n)=(1-temp(n))/blkn_acvar(n)

Add the two images blockwise as follows :-Xij(n)=scalef(n)*Cij(n)+ embedf(n)*Wij(n)

If

L4


3.1.2 Processing colour images

The above algorithm cannot be applied to colour images directly as

colour images are stored in a 3-d array, each dimension representing red,

green and blue (R G B) components .Hence the 3-d array is converted into 2-d

array so that the R G B components can be processed separately using the

above algorithm. In the end the outputs corresponding to each component are

combined to get the watermarked colour image.

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Output the resultant watermarked image

XM

Stop

If

Take IDCT of each 8x8 block in Xij(n) to get the watermarked image XM

n=n+1

L4

L3

Fig 3.6: Flowchart for visible watermarking algorithm


3.2 Invisible watermarking

Invisible watermarking is a technique in which a watermark is

embedded into an image without varying its visual representation, such that

the watermark is perceptually invisible and watermarked image is very much

indistinguishable from original image. The quality of the image as well as the

watermark can be varied by changing the robustness constant. By increasing

this factor the quality of the watermark can be increased but deteriorating the

host image to some extent and vice versa.

Mid-band Coefficient Exchange algorithm [9] is implemented to

achieve invisible watermarking. This algorithm uses the properties of DCT,

and along with them features of Human Visual System are used to achieve

quality invisible watermarking. The implementation has been divided into 2

sections namely,

a. Embedding watermark

b. Extracting watermark

In the first section watermark is inserted in the image suitably by using

DCT. In the second section the inserted watermark is retrieved back. The

original image is not needed in the extraction process but the original

watermark has to be provided .

3.2.1 Description

Mid frequency bands of 2 dimensional DCT

Using 2 dimensional DCT an image can easily be split up in pseudo

frequency bands, making it much easier to embed watermarking information

into the appropriate frequency bands of an image. We define the middle-band

frequencies (FM) of an 8x8 DCT block as shown below in Fig 3.7.

________________________________________________________________________



Fig 3.7 Definition of DCT Regions

FL is used to denote the lowest frequency components of the block,

while FH is used to denote the higher frequency components. FM is middle

frequency components. FM is chosen as the embedding region as to provide

additional resistance to lossy compression techniques [10], while avoiding

significant modification of the cover image. The low frequency band carries

the most important visual parts of the image. On the other hand, the high

frequency band is exposed to removal through compression and noise attacks.

The middle frequency bands avoid the removal through compression as well

as don’t contain important visual information. To prevent an expert from

extracting the hidden information directly from the transform domain, the

watermarks are embedded by modifying the relationship of the neighboring

blocks of mid frequency coefficients of the original image instead of

embedding by an additive operation. Therefore, the middle frequency bands

are the suitable region of watermark insertion.

________________________________________________________________________



Human visual model (HVS) and JPEG quantization table

Invisible watermarking of an image leads to some loss of original data

in the watermarked image compared to original image in order to inherit the

watermark. The data loss must be done selectively and the guiding principle

is to lose data for which the human visual system is not sensitive. The

sensitivity of the HVS to the DCT basis images has been extensively studied,

which resulted in the recommended JPEG (Joint Pictures Experts Group, one

of the most commonly used standards for image compression. The JPEG

quantization table is shown below in Fig 3.8. This table can be used for

predicting and minimizing the visual impact of the distortion caused by the

watermark [10].

Fig 3.8 Quantization values used in JPEG compression scheme

The quantization matrix is the 8x8 matrix of step sizes (also called

quantums) one element for each DCT coefficient. JPEG uses this quantization

table to compress images, in brief different parts of the images are

compressed unequally depending on the quantization values given in the

quantization matrix which was obtained by exploiting HVS. Based on the

table, we can observe that coefficients (4, 1) and (3, 2) or (1, 2) and (3, 0)

would make suitable candidates for comparison, as their quantization values

are equal. The swapping of such coefficients should not alter the watermarked

image significantly, as it is generally believed that DCT coefficients of

middle frequencies have similar magnitudes.________________________________________________________________________



The human eyes are more sensitive to noise in a lower frequency

range than its higher frequency counterpart, but the energy of most natural

images is concentrated in the lower frequency range. The robustness of a

watermark can be improved by increasing the energy of the watermark.

Increasing the energy, however, degrades the image quality. By exploiting the

properties of the HVS, the energy can be increased locally in places where

the human eye will not notice it . As a result, by exploiting the HVS, one can

embed perceptually invisible watermarks that have higher energy than if this

energy were to be distributed evenly over the image.

Design parameters

Maximum size of watermark

N x M is the dimension of the cover image, Size of 2D DCT block is

Blocksize. Then maximum size (Z) of the watermark that can be embedded in

this cover image can be given by

Selection of DCT coefficients.

The two DCT coefficients selected for in the algorithm should be in the

mid frequency band of a 8x8 DCT block. Coefficients corresponding to (4,1)

and (3,2) are selected as both lie in the mid frequency band and have same

quantization values in the JPEG quantization table .

________________________________________________________________________



Watermark strength constant (k)

Watermark strength constant is used to make watermark more robust.

But robustness is obtained at the cost of increased degradation of

watermarked image quality. A reasonable value of k=50 is selected. The value

of ‘k’ should be always positive and can either be increased or decreased as

per the requirements .

3.2.2 Algorithms and flow charts

The algorithms and flowcharts for embedding and extracting the

watermark in invisible watermarking are described in this section.

Algorithm for embedding invisible watermark

1. The cover image (original image) is obtained and its size (i.e. number

of pixels) is calculated.

2. The cover image is divided into 8x8 sub blocks.

3. For each 8x8 block two dimensional DCT block is obtained.

4. The watermark image is obtained and is reshaped in one dimensional

message array.

5. Obtain the size of the message array and check if it is possible to

embed this watermark into the cover image. If possible proceed further

or terminate by flagging a suitable message for the user.

6. The values of the message array are in the range of 0 – 255 since the

image is bit map image represented using 8 bits. The message array is

normalized by dividing entire message array by 255 and rounding off

the values to obtain either 0s or 1s.

________________________________________________________________________



7. If the size of the message array is less than maximum size of the

watermark that can be embedded in given cover image then message

array is appended with either 0s or 1s to maximum size.

8. For a 8x8 DCT block two coefficients B (u1 , v1) and B (u2 , v2) (u ,v

= 0,1,2,3,4,5,6,7) are chosen from the FM region of the 8x8 DCT block

for comparison. These two coefficients are selected on the basis of

JPEG quantization table.

9. To embed a ‘1’ from the message array the coefficients are varied

accordingly to obtain B (u 1 , v1) > B (u 2 , v2). The coefficients are

swapped if the relative size of each coefficient does not agree with the

bit that is to be encoded.

10. Similarly to embed a ‘0’ from the message array the coefficients are

varied accordingly to obtain B (u 2 , v2) > B (u 1 , v1).

11. The robustness of the watermark can be improved by introducing a

watermark “strength” constant k , such that |B (u1 , v1) - B (u2 , v2) | > k.

This is obtained by using B (u1 , v1) = B (u1 , v1) - k /2 and B (u2 , v2) = B

(u2 , v2) + k /2

12. Now this 8x8 block is converted back to spatial domain by performing

2 dimensional Inverse DCT.

13. Steps no.8 to step no.12 are applied to all the 8x8 blocks of the cover

image.

14. The resulting image obtained is the watermarked image which is saved

and then displayed.

Flowchart for embedding invisible watermark

The flowchart for embedding the watermark using the above algorithm

is shown below in Fig 3.9.

________________________________________________________________________



________________________________________________________________________


Start

Input the cover image and the watermark

Size of cover image= N1x M1

Size of watermark = N2x M2

Can watermark of this size be embedded

A

Convert 2D watermark to 1D message array andnormalize and round the values to either 1 or 0

Divide cover image to 8x8 blocks and perform 2 dimensional DCT for each block.

Select a 8x8 block and 2 coefficients in mid frequency band

B

C

No

Yes


Fig 3.9 Flowchart for embedding invisible watermark

________________________________________________________________________


B

Is message bit =’1’

B(u1,v1)>B(u2,v2)

B(u2,v2)> B(u1,v1)

Swap both coefficients

Swap both coefficients

|B(u1,v1)-B(u2,v2)| < k

| B(u2,v2)- B(u1,v1)| < k

B(u1,v1)= B(u1,v1)-k/2B(u2,v2)= B(u2,v2)+k/2

B(u2,v2)= B(u2,v2)-k/2B(u1,v1)= B(u1,v1)+k/2

Perform 2D IDCT for this 8x8 block

Have all 8x8 blocks

operated

Store watermarked image and display

Stop

A

C

Yes

Yes

Yes

Yes

Yes Yes

No

No No

No

No


Algorithm for extracting invisible watermark

1. The watermarked image is obtained and its size is calculated.

2. Calculate the maximum size of watermark that could have embedded in

the watermarked image.

3. Obtain the original watermark from the user and calculate its

dimensions.

4. Check if it was feasible to embed watermark of this size in the cover

image, if no, flag an error message to the user else proceed further with

the remaining steps.

5. The watermarked image is divided into 8x8 blocks.

6. For each 8x8 block two-dimensional DCT block is obtained.

7. For a 8x8 DCT block two locations B (u1 , v1) and B (u2 , v2)

(u ,v = 0,1,2,3,4,5,6,7) are chosen from the FM region of the 8x8 DCT

block for comparison. These two coefficients must be same that were

used in the embedding process.

8. A ‘1’ is extracted if B (u 1 , v1) > B (u 2 , v2) else B(u 2 , v2) > B(u 1 , v1) so

‘0’ is extracted and stored in a one dimensional message array.

9. Step no.7 to step no.8 are applied to all the 8x8 blocks of the

watermarked image.

10. Using the dimensions of the original watermark the extracted

watermark is reconstructed, saved and then displayed.

________________________________________________________________________



Flowchart for extracting invisible watermark

The flowchart for extracting the watermark using the above algorithm

is shown below in Fig 3.10.

________________________________________________________________________


Start

Input the watermarked image and the original watermark

Size of watermarked image= N1x M1

Size of watermark = N2x M2

Can watermark of this size be present in watermarked image

Divide watermarked image to 8x8 blocks and perform 2 dimensional DCT for each block.

Select a 8x8 block and same 2 coefficients in mid frequency band used in embedding

A

Yes

No

B


________________________________________________________________________


A

IsB (u1,v1) > B (u2,v2)

Bit ‘1’ is extracted and stored in 1D message array

Bit ‘0’ is extracted and stored in same 1D message array

Using N2, M2 extracted watermark is reconstructed and stored

Display watermark

Stop

B

Yes No

Fig 3.10 Flowchart for extracting invisible watermark


Chapter 4

RESULTS

________________________________________________________________________



RESULTS

The algorithms were successfully implemented on the MATLAB

platform. We discuss the results obtained for both visible and invisible

watermarking implementations in this chapter.


The algorithm for visible watermarking was implemented using Fig 4.1

and Fig 4.4 as the cover images and Fig 4.2 and Fig 4.5 as the watermarks.

Fig 4.3 and Fig 4.6 shows the resultant watermarked images. The scaling

factor has been reduced and embedding factor has been increased

considerably for increasing perceptibility.

Fig 4.1 Original image Fig 4.2 Watermark image

________________________________________________________________________



Fig 4.3 Watermarked image

Fig 4.4 Original colour image Fig 4.5 Watermark colour image

________________________________________________________________________



Fig 4.6 Watermarked colour image


The algorithm has been implemented using the original image shown in

Fig 4.7 and watermark image shown in Fig 4.8. The watermark strength

constant (k) has been varied, and watermarked image obtained for different

cases for k = 0, 1, 50, 100 has been shown in Fig 4.9 to Fig 4.12 respectively.

The original and watermarked images shown in below figures are

scaled to 50% of the original size whereas the original size of watermark and

recovered watermark is retained for proper display.

________________________________________________________________________



Fig 4.7 Original image Fig 4.8 Watermark image

Case 1: K=0

Fig 4.9a Watermarked image Fig 4.9b Recovered watermark

Case 2: K=1

________________________________________________________________________




Case 3: K=50


Case 4: K=100

________________________________________________________________________




Chapter 5

________________________________________________________________________



CONCLUSION AND FUTURE

SCOPE

CONCLUSION AND FUTURE SCOPE

The algorithms were implemented and satisfactory results were

obtained. This chapter deals with the applications, merits and demerits, and

scope for future work [7,9].


The advantages and disadvantages of the visible watermarking

algorithm we implemented are given below.

The advantages are

________________________________________________________________________



1. Resistant to lossy compression, the watermark does not get altered even

if the watermarked image undergoes compression.

2. It is fairly robust.

3. Fidelity is fairly high.

The disadvantages are

1. Susceptible to image cropping

2. As the watermark is perceptible the commercial value of the image is

reduced.


The advantages and disadvantages of the invisible watermarking

algorithm we implemented are given below [10].

The advantages are

1. The watermark can be extracted from the watermarked image without

reference to original image .

2. Resistant to lossy compression like JPEG, the watermark does not get

altered even if the watermarked image undergoes compression.

3. Can withstand filtering attacks.

The disadvantages are

1. Susceptible to image cropping and geometric distortions like scaling

and rotation.

2. Grey levels in watermark image should preferably be at max (255)

or min (0) limits for better results.

3. Cannot be applied to colour images.

5.3 Applications

Fingerprinting . This application allows acquisition devices (such as

Digital cameras) to insert information about the specific device (e.g.,

an ID number) and date of creation. This can also be done with

________________________________________________________________________



conventional digital signature techniques but with watermarking it

becomes considerably more difficult to excise or alter the signature

[1,7,8].

Authentication . Watermarking has two major benefits as compared to

cryptography. First, the watermark becomes embedded in the message,

secondly, it is possible to create ‘soft authentication‘ algorithms that

offer a multivalued measure that accounts for different (un)intentional

transformations that the data may have suffered (like compression with

different levels), instead of the yes/no answer given by cryptography-

based authentication..

Copy and Playback Control . The message carried by the watermark

may also contain information regarding copy and display permissions.

Then, a secure module can be added in copy or playback equipment to

automatically extract this permission information and block further

processing if required. In order to be effective, this protection

approach requires agreements between content providers and consumer

electronics manufacturers to introduce compliant watermark detectors

in their digital cameras and other displays.

Signaling . In case of invisible watermarking, the imperceptibility

constraint is helpful when transmitting signaling information in the

hidden channel. The advantage of using this channel is that no

bandwidth increase is required.

Broadcast monitoring. Watermarking can be used for broadcast

monitoring by putting a unique watermark in each image prior to

broadcast. Automated monitoring stations can then receive broadcasts

and look for these watermarks, identifying when and where each

watermarked image appears. Thus advertisers, musicians, actors,

copyright owners are sure that their property is not illegally

rebroadcast by pirate stations. Commercial systems have been deployed

using this approach for a number of years.

________________________________________________________________________



Proof of ownership . Multimedia owners can use watermarks not just to

identify copyright ownership, but to actually prove ownership.

Traditionally, the image has to be registered with the Copyright Office

by sending a copy to them. The Copyright Office archives the image,

together with information about the rightful owner. When the disputes

occur the Copyright Office can be contacted to obtain prove ownership.

But when not registered, it is possible to use a watermark embedded in

the image to prove ownership.

5.4 Scope for future work

The project has a considerable scope for improvement in many

dimensions. A few are listed below:

1. The algorithm can be implemented in real-time by targeting on to a

DSP/FPGA with reduced delay.

2. The fidelity of the watermarked image can be improved by using other

transforms such as wavelets, as HVS can be closely analyzed by them.

3. Multiresolution watermarking can be done using wavelets.

________________________________________________________________________



Chapter 6

________________________________________________________________________



BIBLIOGRAPHY

BIBLIOGRAPHY

Papers

1. Ingemar J. Cox, Matt L. Miller and Jeffrey A. Bloom, Watermarking

applications and their properties , published in the International

Conference on Information Technology’2000, Las Vegas, 2000.

2. Frank Hartung, student member, IEEE, and Martin Kutter, Multimedia

Watermarking Techniques , Proceedings of the IEEE, Vol. 87, No. 7,

July 1999.

________________________________________________________________________



3. Fernando Perez-Gonzalez and Juan R. Hernandez, A Tutorial on Digital

Watermarking.

4. Syed Ali Khayam, The Discrete Cosine Transform (DCT): Theory and

Applications.

5. Johnson C. Lee, Student Member IEEE, Analysis of Attacks on Common

Watermarking Techniques.

6. Saraju P. Mohanty, K.R. Ramakrishna and Mohan S Kankanhalli , A

DCT Domain Visible Watermarking Technique for Images.

7. Pei_Chun Chen, On the Study of Watermarking Application in

WWW_Modeling_ Performance Analysis_ and Applications of Digital

Image Watermarking Systems, Master Thesis of Department of

Electrical Engineering, National Tsing Hua University May 1999.

8. Raymond B. Wolfgang and Edward J. Delp, A Watermark for Digital

Images.

9. H. I. Saleh, M. E. Elhadedy, M. A. Ashour, M. A. Aboelsaud .

Comparison of DCT-based and DWT-based watermarking technique.

10. Gerhard C. Langelaar, Iwan Setyawan, and Reginald L. Lagendijk,

Watermarking Digital Image and Video Data, IEEE signal processing

magazine SEPTEMBER 2000.

11. Robert B. Sweig , Copyright Protection of Digital Still Images Using

Invisible Watermarking Techniques

12. Koch, E., & Zhao, J. (1995), Towards robust and hidden image

copyright labeling , Proceeding of IEEE Nonlinear Signal Processing

Workshop, (pp. 452-455).

________________________________________________________________________



Books

13. Ze-Nian Li and Mark S. Drew; Fundamentals of Multimedia ,

PEARSON Prentice Hall, 2004.

14. Rafael C. Gonzalez and Richard E. Woods; Digital Image Processing ,

PEARSON Prentice Hall, 2 n d edition 2005.

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