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CHAPTER 1

INTRODUCTION

1.1 GENERAL

The protection of intellectual property has become a major problem

in the digital age. The ease of copying digital information without any loss of

quality violates the conservation of mass property of traditional media, which

inhibited wide global distribution in the past. On the Internet today, it is

possible to duplicate digital information a million-fold and distribute it over

the entire world in seconds. These issues worry creators of intellectual

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

More information is transmitted in a digital format now than ever, and the

growth in this trend cannot be estimated in the future. Digital information is

susceptible to be copied at the same quality as the original. A watermark is a

pattern of bits inserted into a digital image, audio or video file that identifies

the file's copyright information (author, rights, etc.). The name “watermark” is

derived from the faintly visible marks imprinted on the organizational

stationary.

During the 18th century watermarks began to be used as anti-

imitation measures on money and other documents. When sharing

information on the internet, digital watermark approaches are of great

demand. While distributing information through online, we never know if

someone uses them without our knowledge. The owner should be able to hide

some information in the digital file and extract information to prove his

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ownership when the need arises. Watermarking system can be viewed as a

communication system consisting of three main elements: an embedded, a

communication channel and a detector. To make use of the Human Visible

System (HVS), various watermarking techniques have been developed.

Figure 1.1 shows a general watermarking life cycle. Tracking of

reproduced copies, prevention of illegal copying and validating the digital

data can be done by the watermark. Insertion of a watermark, detection of a

watermark and removal of a watermark are the three main processes involved

in a watermarking system.

Figure 1.1 General watermarking life cycle

Important characteristics of the watermark are invisibility,

robustness, readability and security (Ming-Shing et al 2001, Sin and sung

2001). Requirement for digital watermarks are 1) deterioration of the quality

of digital content is minimized 2) watermarks are retained and detectable after

the digital content is edited, compressed, or converted 3) the structure of a

watermark makes it difficult to detect or overwrite (alter) the embedded

information (watermark contents) 4) processing required for watermarking

and detection is simple 5) watermark information embedded in digital content

can be detected as required and 6) embedded watermark information cannot

Secure Part – Transmitter In secure Part

Secure Part – Receiver

Attacks

Original Image

EmbeddingScheme

Embedding

Detection

Retrieval

Decoding

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be eliminated without diminishing the quality of the digital content that

carries the watermark.

1.2 CLASSIFICATION OF WATERMARK

General classification of the watermark is shown in Figure 1.2.

Watermarking is classified based on working domain, type of document,

human perception and application. Watermarking techniques are divided into

four categories in accordance with the type of information (document) to be

watermarked (Laurence and Ahmed 1996). They are text watermarking,

image watermarking, audio watermarking and video watermarking. In text

watermarking, the text documents can be watermarked by patterning the inter-

word spaces. Text watermarking is primarily of three types: Line Shift

Coding (LSC), Word Shift Coding (WSC) and Feature Coding (FC). These

methods require the original unmarked text for decoding.

Figure 1.2 General classification of watermarking

WATERMARKING

According To Human Perception

According To Working Domain

According To Application

Spatial Domain

Frequency Domain

Invisible

Visible Source Based

Destination Based

Robust Fragile

Private Public Quasi-invertible

Invertible Non-invertible

Non quasi-invertible

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In image watermarking technique, the watermark image is applied

into a host image for security. Image watermarking is nothing but the still

image watermark. A continuous frame of image is called as video and the

watermarking process is known as video watermarking. Digital video

watermarking uses the inherent properties of digital images, with the

limitations of human vision to insert invisible data into digital video to

provide copyright protection. Based on the visibility of the resultant image,

the digital watermarks can be divided into two different categories viz. visible

watermark and invisible watermark. Visible watermark is a secondary

translucent overlaid into the primary image but in an invisible digital

watermarking, information is added as digital data.

In the case of audio watermarking, to hide the watermark and make it

inaudible, watermarking uses the time and frequency masking properties of

the human ear. Echo hiding is one of the techniques which involve hiding

information within the recorded sound by introducing very short echoes.

Invisible digital watermark is further divided into private and public

watermarking. In private watermarking or informed watermarking, the

original image is required to perform the extraction process. In public

watermarking or blind watermarking, the original image is not required to

perform the extraction process. In the public watermarking process,

watermarked images are seriously destroyed and the detection of watermarked

image is very difficult. Because of this, blind watermarking technique is used

for visible watermarking.

Based on the ability of the watermark to resist attack, watermarks

are categorized into two types. They are fragile watermark and robust

watermark. Random image processing methods can readily destroy the fragile

watermarks. Most of the image processing methods are robust and can be

extracted from heavily attacked watermarked image without destroying the

image. This makes the robust watermark to be preferred in copyright

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protection. Invisible–robust watermark is embedded in such a way that the

alterations made to the pixel value are perceptually not detected and it can be

recovered only with appropriate decoding mechanism. Invisible–fragile

watermark embedding process of the image would alter or destroy the

watermark. Watermarking techniques are frequently used in the still camera

images, medical images and satellite images where the copyright protection is

required by the users.

A digital image is usually represented by a two-dimensional image.

Depending on the image resolution, an image may be a vector or a raster in

type. Digital image usually refers to raster images and it is also called as

bitmap images. Various available digital image file types are Joint

Photographic Groups (JPG), Graphic Interchange Format (GIF), Tagged

Image File Format (TIFF), Portable Network Graphics (PNG), and Bitmap

(BMP). TIFF is a very flexible format that can be lossless or lossy. PNG is a

lossless storage format; it can be used to compress the file size. The

compression is exactly reversible, so the image is recovered exactly. GIF is

lossless only for images with 256 colour or less. JPG works by analyzing

images and discarding kinds of information. BMP is an uncompressed

proprietary format.

Digital Still Camera (DSC) records the image data in the form of

document, specified as the standard file format. Nowadays, digital documents

can be distributed via the World Wide Web (WWW) to a large number of

people in a cost-efficient way. There is a strong need for security services in

order to keep the distribution of digital multimedia work both profitable for

the document owner and reliable for the customer. Watermarking technology

plays an important role in securing the business as it allows placing an

imperceptible mark in the multimedia data to identify the legitimate owner,

track authorized users via fingerprinting (Dittmann 1999) or detect malicious

tampering of the document (Kundur and Hatzinakos 1998).

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Patient records are stored in hospitals in digital format (Electronic

Patient Records (EPR)) for more than 20 years. Medical image has three

binding security characteristics, such as confidentiality, availability and

reliability. In our proposed work on confidentiality, we use public key.

Availability can be proved by decoding the watermarked image by using

normal procedure. Reliability will be proved by the information which cannot

be modified by an unauthorized person. Reliability is of much importance as

degradation of the image content will lead to serious problems such as wrong

diagnosis of a patient by the doctor. Thus, watermarking is important in case

of medical images for determining authenticity.

Remote sensing satellite images are important sources of

geographical data. Geographical data are commonly used to classify earth

land cover, analyze crop conditions, assess mineral, petroleum deposits, and

quantify urban growth. Contrast stretching, flipping and format conversion

are the attacks that easily remove the watermark image in a satellite image.

An effective watermarking technique for satellite images should have the

following features: The watermark should be imperceptible to the naked eye.

The watermark must be indelible, at least without visibly degrading the

original image. Retrieval of the watermark should explicitly identify the

owner. The watermarking technique should not distort certain specific areas

in the image. Stir mark is commonly used to evaluate the robustness of an

image (Evelyn et al 2009).

1.3 WATERMARKING TECHNIQUES

Digital image watermarking schemes mainly fall into two broad

categories: spatial domain and frequency domain techniques. Visible

watermarking mainly uses spatial domain which requires less computation

and are easy to implement in software as well as hardware. A spatial domain

technique slightly modifies the pixels. However, there must be tradeoffs

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between invisibility and robustness, and it is hard to resist common image

processing and noise. Some of the spatial domain modulation techniques are

Least Significant Bit (LSB), Spread Spectrum Method (SSM). In LSB, the

watermarks are embedded in the least significant bit of the selected pixels of

an image. This method is easy to implement and it is not very robust against

attacks. SSM based watermarking algorithms embed the information by

linearly combining the host image with a small pseudo noise signal, which is

modulated by the embedded watermark.

Compared to spatial domain methods, frequency domain methods

are more widely applied. In frequency domain, the characteristics of the HVS

are better captured by the spectral coefficients. For example, HVS is more

sensitive to low frequency coefficients and less sensitive to high frequency

coefficients. Low frequency coefficients are perceptually significant, which

means alterations to those components might cause severe distortion to the

original image. On the other hand, high frequency coefficients are considered

insignificant and hence the processing techniques, such as compression, tend

to remove high frequency coefficients assertively. To obtain a balance

between imperceptibility and robustness, most watermark algorithms are

embedded in the midrange frequencies. Commonly used frequency domain

techniques are DCT, Discrete Fourier Transform (DFT) and Discrete Wavelet

Transform (DWT).

DCT based watermarking techniques are robust compared to spatial

domain techniques. DCT algorithms are robust against simple image

processing operations like low pass filtering, brightness and contrast

adjustment, blurring, etc. DCT watermarking techniques are difficult to

implement and are computationally more expensive. They are also weak

against geometric attacks like rotation, scaling, cropping, etc. DCT

watermarking can be classified into global DCT watermarking and block

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based DCT watermarking. DCT based watermarking are affected by two

factors. The first fact is that the most important visual part of the image lies at

low frequency sub-band. The second fact is that high frequency components

of the images are usually removed through compression and noise attacks.

DCT watermark is therefore embedded by modifying the coefficients of the

middle frequency sub-band. DFT of a function gives quantitative results of

the frequency content in terms of magnitude and phase. This result is more

important for processing and analysis of signals and images. The DWT is

currently used in a wide variety of signal processing applications, such as in

audio and video compression, removal of noise in audio, and the simulation of

wireless antenna distribution (Evelyn et al 2009). In wavelets, basal functions

are used to represent the signal. DWT is very suitable to identify the areas in

the host image, where the watermark image can be embedded. Wavelets have

their energy concentrated in time and are well suited for the analysis of

transient and time-varying signals.

Watermarking techniques have got a number of applications. Some

of the significant applications are fingerprint, prevention of unauthorized

copying, image authentication, data security, digital media management,

medical area and copyright protection. Copyright protection is probably the

most common use of watermarks today. Copyright owner information is

embedded in the image in order to prevent others from alleging ownership of

the image. Copyright-related applications based on robust watermarking

techniques were discussed by many researchers like Barni et al (2002),

Moulin and Ivanovic (2003), Sebe and Domingo (2003), Trappe et al (2003).

Medical reports play a very important role in the treatments offered to the

patient. A mix up in the reports of two patients could lead to a disaster. To

avoid this problem, visible watermarking technique is used to print the names

of the patients on the X-ray or Magnetic Resonance Image (MRI) scan

reports. Fragile or semi-fragile watermarks are usually selected for

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watermarking process in medical, forensic and intelligence or military

applications (Barreto et al 2002, Li and Yang 2003, Li 2004, Wong and

Memom 2000, Xie and Arce 2001).

In data encryption (embedding), techniques of digital watermarking

do not follow with the same capability because listening, accessing and

viewing the content cannot be prevented. For this reason, digital

watermarking is not protected from hacker attacks (Yeung et al 1998). Some

of the intentional attacks on watermarks are active, passive, forgery and

collusion attacks (Cox et al 2000). In active attacks, the hacker removes the

watermark or makes it undetectable. In passive attacks, the hacker can easily

identify the presence of watermark in the original image without any damage

or removal. The hacker attempts to embed a valid watermark of their own

rather than removing the original watermark in forgery attacks. One piece of

the media is replicated into several copies, each with a different watermark, in

order to construct a copy with no watermark due to collusion attacks.

1.4 PERFORMANCE ANALYSIS

Performance analysis is needed to determine the characteristics of

the watermarking technique such as imperceptible, indelible, statistically

undetectable and easily decodable. Popular metrics used for evaluating

imperceptibility of the watermark are Signal-to-Noise Ratio (SNR) and Peak

Signal-to-Noise Ratio (PSNR), which are based on Mean Square Error (MSE)

between the original and watermarked images. Image manipulation tool (stir

mark) is used to measure the effectiveness of watermark embedding technique

in terms of its robustness and data integrity criteria. Pixel based visual

distortion metrics (Kutter and Petitcolas 1999) are used for performance

analysis to test the image quality between the original and the watermarked

images.

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Correlation coefficient is essential for mapping and ranging

purposes. Individual quality measures are not reliably associated with the

strength of treatment effect in medical areas. Although the use of specific

quality measures may be appropriate in specific well-defined areas of the

medical field, it cannot be generalized to all clinical areas or meta-analysis

(Pan et al 2004). Normalized Correlation Coordinate (NCC) computes the

similarity measurement between the original watermark and the extracted

watermark. Image Fidelity (IF) is a process used to deliver an image

accurately, without any distortion or information loss. IF output depends upon

the ability to detect the difference between images (Klimeck et al 2002). If the

difference between an original image and a compressed one cannot be

detected, then it is concluded that the compression is a lossless compression.

SNR measures are easy to estimate the quality of a reconstructed image

compared to the original image.

Peak signal of the reconstructed watermark image is measured by

PSNR. PSNR values are measured in decibels. Typical PSNR values range

between 20dB and 40dB. The actual value is not meaningful, but the

comparison between two values for different reconstructed images gives a

measure of quality. MSE gives the results of degradation, which was

introduced at the pixel level. The higher MSE shows more degradation.

Accuracy Rate (AR) is used to measure the difference between the original

watermark and the recovered one. AR is computed as follows: AR= CP/ NP.

Where NP is the number of pixels in the original watermark and CP is the

number of correct pixels.

1.5 PROBLEM FORMULATION

This thesis aims at developing an efficient hardware architecture for

the implementation of visible watermarking technique in both spatial domain

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and frequency domain and also aims at the performance analysis of the

algorithm used for invisible watermarking technique using MATLAB 7.6.

The vector based visible digital image watermarking algorithm

using 1D-DCT is tested and implemented with reduced computational

complexity and resource utility involving the scaling

embedding

computational complexity. With this implementation, the speed and

throughput are increased. In biomedical applications, small distortions in the

host image make more problems while diagnosing the diseases. On focusing

biomedical applications, a new block based visible image watermarking

algorithm is developed. In block based a fast 1D-DCT is used to reduce the

resource utilization. In addition, a new mathematical model is introduced to

find the values of scaling and embedding factors. Quality image can be

obtained by means of combining various watermarking techniques. A new

watermarking system is designed to combine the spatial and the frequency

domain techniques.

For the above proposed works, we developed a novel high

performance VLSI architecture implemented on FPGA, simulated in Xilinx

ISE 10.1 and tested in Xilinx Virtex V XC5V1X330 technology. In order to

achieve high throughput and speed, the architectures are designed with the

implementation of pipelining and parallelism techniques.

The hardware architecture designed is applied for visible

watermarking technique only. In order to touch the other category of

watermarking, namely the invisible watermarking, a movement based

watermarking algorithm is developed. The performance analysis of this

algorithm is obtained using MATLAB 7.6.

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Initially, the performance analysis of both visible and invisible

watermarking scheme were computed using the software MATLAB 7.6 and

the evaluation of the works based on synthesis were done using the Xilinx

tool ISE 10.1. Finally, the throughput for visible watermarking is compared

with that of the existing hardware implementation.

1.6 THESIS ORGANIZATION

Chapter 2, “Literature review”, presents a detailed literature review

of the digital watermarking, the existing watermarking algorithms and the

spatial and frequency domain watermarking techniques. It also presents the

reviews related to the hardware implementations.

Chapter 3, “Design and VLSI implementation of vector based

visible image watermark using 1D-DCT”, describes the architecture design

for vector based digital image watermarking. For this, the algorithm is

designed to aim at reducing the computational complexity involving the

embedding and scaling factors prominently used in any visible watermarking

technique.

Chapter 4, “High performance VLSI architecture for block based

visible image watermarking”, explains VLSI architecture design and

implementation of block based visible image watermarking algorithm and its

performance analysis. The fast 1D-DCT for watermarking process is

introduced to facilitate the hardware implementation.

Chapter 5, “Design and implementation of hybrid VLSI

architecture for visible spatial and frequency domain watermarking”,

describes VLSI architecture design and implementation of visible spatial and

frequency domain watermarking algorithms and their performance analysis.

Based on choice of watermark, the process is done either as a pixel by pixel

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operation under spatial domain or as a vector form of operation under

frequency domain.

Chapter 6, “Performance analysis for geometrical attacks on digital

image watermarking”, describes performance analysis for geometrical attacks

on digital invisible image watermarking. Here, the irreversible watermarking

approach robust to affine transform attacks is used. In this approach,

watermark embedding and extraction are carried out with respect to an image

normalized to meet a set of predefined moment criteria.

Chapter 7, “Conclusion”, summarizes the contribution of this thesis

by the implementation of the three proposed approaches in the hardware, the

algorithm proposed for invisible watermarking and its performance

evaluation. Suggestions for future work are also included.