Robust Digital Image Watermarking Computer Science Essay

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The copyright protection of digital content became a critical issue now a days due to the internet does not use secure links, thus information in transmission is vulnerable to interception. Some solution to be discussed to transmit information in such a manner that the very existence of the multimedia content is unknown to unauthorized users in order to repel their attention. Some important disciplines of information hiding are cryptography, steganography and watermarking. While cryptography is about protecting the content of the text messages, steganography is about concealing their very existence. Watermarking is about hiding multimedia content in other multimedia data. Watermarking and cryptography are closely related, but cryptography scrambles the image so that it cannot be understood. Similar to steganography, watermarking is about hiding information in other image, but difference is that watermark must be somewhat resilience against attempts to remove it. The approach of information hiding can be extended to protect the copyright of multimedia content.

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Digital image watermarking technique embeds copyright information into multimedia content. In this research work, two techniques are proposed for information hiding. The first approach is the robust digital image watermarking scheme using back propagation neural network in discrete wavelet transform domain. In this approach, the cover image decomposed into red, green, and blue planes, and the blue plane is divided into 8x8 blocks. Human Visual System is insensitive to variations in blue plane, hence blue plane is selected to embed watermark. The third level discrete wavelet transform is performed on each block. The discrete wavelet transform provides multi-resolution of an image, so that the image can be processed sequentially from low resolution to high resolution. The bitmap is selected as watermark and embedded into high and middle frequency coefficients of blue plane of cover image using trained back propagation neural network. The advantage of back propagation neural network is that the errors are back propagated so that the weights of the layers are adjusted continuously until getting the desired output. The aim of this network is to train the net to achieve the balance between the ability to respond correctly to the input pattern that are used for training and the ability to provide good response to the input that are similar. Back Propagation Neural Network has good nonlinear approximation ability. It can establish the relationship between original wavelet coefficients and watermarked wavelet coefficients by adjusting the network weights and bias before and after embedding watermark. The robustness of the watermarked image is tested by some normal attacks such as JPEG compression, median filtering, cropping, salt&pepper noise, and rotation. The similarity between the embedded watermark and extracted watermark is tested by measuring the normalized correlation coefficient. The proposed blind watermarking algorithm is robust to cropping, salt&pepper noise and rotation attacks, but weak to JPEG compression and median filtering attacks.

The second approach is the robust digital image watermarking scheme using quantization and dynamic fuzzy inference system. The Dynamic Fuzzy Inference System (DFIS), also known as Dynamic Fuzzy Expert System, is a widely accepted computing framework based on the popular concepts of fuzzy set theory, fuzzy if-then rules and fuzzy reasoning. In this method, the mamdani type fuzzy method is exploited to determine a valid approximation of quantization step of each DWT coefficient. A rule base is developed to quantize the wavelet coefficients. The basic concept of fuzzy inference system is that variable values are either words or linguistic variables rather than numbers, their use is closer to human intuition. Computing with either words or linguistic variables exploits the tolerance for imprecision and there by lowers the cost of solution. The watermark is embedded into high and middle frequency sub bands of the wavelet transformed coefficients of the blue plane of the cover image, using dynamic fuzzy inference system. The imperceptibility of the watermarked image is tested by measuring peak signal to noise ratio. The robustness of the watermarked image is tested by performing different attacks such as JPEG compression, median filtering, cropping, salt&pepper noise and rotation. However, this approach is weak to median filtering attack. The similarity between inserted watermark and extracted watermark is tested by measuring normalized correlation coefficient.

Finally, the mean square error, the peak signal to noise ratio and normalized correlation coefficients of two methods are compared and results show that the DFIS algorithm provides better results than BPNN algorithm, except for median filtering attack.

CONTENTS

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ACKNOWLEDGEMENT

ABSTRACT

LIST OF FIGURES

LIST OF TABLES

LIST OF SYMBOLS & ACRONYMS

1. INTRODUCTION

1.1 Overview of watermarking

1.2 Motivation

1.3 Organization of the work

2. LITERATURE REVIEW

2.1 Digital Steganography

2.1.1 Properties of Steganography

2.1.2 Applications of steganography

2.2 Digital Image Watermarking

2.2.1 Properties of Digital Image Watermarking

2.2.2 Applications of Digital Image Watermarks

2.2.3 Key differences between watermarking and Steganography

2.3 Digital Image Watermarking algorithms

2.3.1 Spatial Domain Techniques

2.4 Research objectives

2.5 Problem statement

2.6 Chapter summary

3. DIFFERENT DOMAINS OF WATERMARKING

3.1 Digital Image Watermarking using 2-D Fourier Transforms

3.2 Digital Image Watermarking using Wavelets

3.3 Discrete Wavelet Transform (DWT)

3.4 Different Techniques to optimize watermarking

4. DIGITAL IMAGE WATERMARKING USING BPNN

4.1 Back Propagation Neural Network (BPNN)

4.2 Watermark embedding using BPNN

4.3 watermark extraction using BPNN

4.4 Watermark attacks on BPNN method

4.5 Comparison of attacks in BPNN method

4.6 Chapter summary

5. DIGITAL IMAGE WATERMARKING USING DFIS

5.1 Watermark embedding using DFIS

5.2 Watermark extraction using DFIS

5.3 Watermark attacks on DFIS method

5.4 Comparison of attacks in DFIS method

5.5Chapter summary

6. COMPARISON OF BPNN AND DFIS METHODS

6.1 Comparison of JPEG compression attacks

6.2 Comparison of cropping attacks

6.3 Comparison of median filtering attacks

6.4 Comparison of rotation attacks

6.5 Comparison of salt&pepper noise attacks

6.6 Chapter summary

7. CONCLUSIONS AND FUTUREWORK

REFERENCES

CONFERENCES AND PUBLICATIONS

List of Figures

List of Tables

List of Symbols and Acronyms

CHAPTER1

INTRODUCTION

OVERVIEW OF WATERMARKING

Due to the rapid and massive development of multimedia and the widespread use of the Internet, there is a need for efficient, powerful and effective copyright protection techniques. A variety of image watermarking methods have been proposed, where most of them are based on the spatial domain or the transform domain. However, in recent years, several image watermarking techniques based on the transform domain are developed [1].

Digital Image watermarking schemes are typically classified into three categories. Private watermarking which requires the prior knowledge of the original information and secret keys at the receiver. Semi private or semi blind watermarking where the watermark information and secret keys must be available at the receiver. Public or blind watermarking where the receiver must only know the secret keys [2]. The robustness of private watermarking schemes is high to endure signal processing attacks. However, they are not feasible in real applications, such as DVD copy protection where the original information may not be available for watermark detection. On the other hand, semi-blind and blind watermarking schemes are more feasible in that situation [3]. However, they have lower robustness than the private watermarking schemes [4]. In general, the requirements of a watermarking system fall into three categories: robustness, visibility, and capacity. Robustness refers to the fact that the watermark must survive against attacks from potential pirates. Visibility refers to the requirement that the watermark be imperceptible to the eye. Capacity refers to the amount of information that the watermark must carry. Embedding a watermark logo typically amounts to a tradeoff occurring between robustness, visibility and capacity.

Digital image watermarking is a kind of technology that embeds copyright information into multimedia content. Any type of image watermarking scheme mainly includes watermark generation, watermark embedding, watermark detection, and watermark attack [5].Digital image watermarking provides copyright protection to an image by hiding appropriate information into cover image to declare rightful ownership of authenticated users [6]. There are four essential factors which are commonly used to determine the quality of the watermarking scheme. They are robustness, imperceptibility, capacity, and blindness. Robustness is a measure of immunity of watermark against attempts to image modification and manipulation like compression, filtering, rotation, scaling, noise attacks, resizing, cropping etc. Imperceptibility is the quality of the cover image that should not be destroyed by the presence of watermark. Capacity is the amount of information that can be embedded in to cover image. Extraction of watermark from the watermarked image without the need of original image is referred to as blind watermarking. The non-blind watermarking technique requires the original image to detect and extract the watermark from the watermarked image. The semi-blind watermarking scheme requires the secrete key and watermark bit sequence for extraction. Another categorization of watermarks based on the embedded data is visible or invisible [7].

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According to the domain of watermark insertion, the watermarking techniques fall into two categories: spatial domain methods and transform domain methods. Many techniques have been proposed in the spatial domain such as LSB (Least Significant Bit) insertion method, the patch work method and the texture block coding method [8]. These techniques process the location and luminance of the image pixel directly. The LSB method has a major disadvantage that the least significant bits can be easily destroyed by lossy compression. Transform domain method based on special transformations, and process the coefficients in frequency domain to hide the data. Transform domain methods include Fast Fourier Transform(FFT), Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT), Curvelete Transform(CT), Counterlet Transform(CLT) etc. In these methods the watermark is hidden in the high and middle frequency coefficients of the cover image. The low frequency coefficients are suppressed by filtering as noise, hence watermark is not inserted in low frequency coefficients [8]. The transform domain method is more robust than the spatial domain method against compression, filtering, rotation, cropping and noise attack etc.

Yonghong Chen and jiancong Chen [9] presents a blind image watermarking scheme that embeds watermark messages at different wavelet blocks is presented base on the training of BPNN in wavelet domain. He Xu, Chang Shujuan [10] presents an adaptive image watermarking algorithm which is based on synthetic human visual system characteristic and associative memory function of neural network. N.Chenthalir Indra and Dr. E. Ramraj [11] proposed a system SBS-SOM a neural network algorithm was trained to generate digital watermark values from the image. Chen Yongqinang, Zhang Yanqing, and Peng Lihua [5] presents a DWT domain image watermarking scheme, where genetic algorithm is used to select the wavelet coefficients to embed watermarking bits into the cover image. Samesh Oueslati, et al. [13] presents an adaptive image watermarking scheme based on Full Counter Propagation Neural Network. Maher EL` ARBI, Chokri BEN AMAR and Henri NICOLAS [14] proposed a novel approach to neural network watermarking for uncompressed video in the wavelet domain. Summrina Kanwal Wajid et al [15] proposed the robust and imperceptible image watermarking using Full Counter Propagation Neural Network with lesser complexity and easy apprehension. Cheng Ri.Pia, et.al [16] proposed a new blind watermark embedding/extracting algorithm using the Radial Basis Function Neural Network. Pao-Ta.Yu et al [17] developed watermarking techniques, integrating both color image processing and cryptography, to achieve content protection and authentication for color images.

The efficiency of any watermarking method is mainly based on the performance metrics like Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC).

MOTIVATION

Based on the above study, it is inferred that the transform domain is better suited for watermarking. The various methods conclude that the wavelet transform sub band resolution is a better method for watermark insertion.

Watermarking in DWT domain has numerous advantages over other transforms; particularly the Discrete Cosine Transform (DCT).Wavelet transformed image is a multi-resolution description of an image. Hence, an image can be shown at different level of resolution and can be sequentially processed from low to high resolution. DWT is closer to the properties of the human visual system than the DCT, as the selection of embedding is flexible by splitting the signal into individual bands. DWT watermarking techniques follow Human Visual System (HVS) characteristics; it is difficult to detect the watermark existence in the cover image. The high frequency area should be avoided for better robustness while the low frequency area should be avoided for low fidelity. Recent work has focused on developing methods for embedding watermarks in the middle frequency, as it provide a good trade-off between robustness and fidelity.

A neural network represents a highly parallelized dynamic system with a directed graph topology that can receive the output information by means of reaction of its state on the input nodes. Back Propagation Neural Network (BPNN) has good nonlinear approximation ability. It can establish the relationship between original wavelet coefficients and watermarked wavelet coefficients by adjusting the network weights and bias before and after embedding watermark.

The Dynamic Fuzzy Inference System (DFIS), also known as Dynamic Fuzzy Expert System, is a widely accepted computing framework based on the popular concepts of fuzzy set theory, fuzzy if-then rules and fuzzy reasoning. Mamdani type DFIS model is exploited in order to determine a valid approximation of a quantization step of each DWT coefficient. Furthermore, the HVS properties are modeled using bi-orthogonal wavelets to improve watermark robustness and imperceptibility.

The efficiency of a digital image watermarking process is evaluated based on the properties of perceptual transparency, robustness, computational cost, capacity of data embedding process, recovery of watermark with or without access to the cover image and the tradeoff between capacity, robustness and imperceptibility.

These features have motivated to develop two new methods for watermarking in transform domain using Back Propagation Neural Network (BPNN) and Dynamic Fuzzy Inference System (DFIS).

ORGANIZATION OF THE WORK

The work is organized into seven chapters as follows:

Chapter 1 presents an overview of digital image watermarking and motivation of the current work.

Chapter 2 provides a survey of the related works on Digital Image watermarking, different watermarking algorithms using Image Transforms, Wavelets, with different Neural Networks and Fuzzy Logic approaches, along with their applications, limitations and objectives of the current research work.

Chapter 3 describes the Discrete Wavelet Transform (DWT), Back Propagation Neural Network (BPNN) and Dynamic Fuzzy Inference System (DFIS) and their implementation to perform watermarking.

Chapter 4 concentrates on providing the implementation details of Back Propagation Neural Network (BPNN) in DWT to embed watermark along with experimental results. The efficiency of the algorithm is tested by performing various attacks such as JPEG compression, Median filtering, Cropping, salt& pepper noise and rotation.

Chapter 5 dwells the implementation of the watermarking algorithm in DWT domain using Dynamic Fuzzy Inference System (DFIS). The efficiency of the algorithm is tested by performing various attacks such as JPEG compression, Median filtering, Cropping, salt& pepper noise and rotation.

Chapter 6 compares the Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC) of the BPNN and DFIS methods. The proposed methods are designed and implemented using MATLAB 7.8.

Chapter 7 explains the results and conclusions with limitations and future work in detail.

CHAPTER 2

LITERATURE REVIEW

This chapter reviews the appropriate background literature and describes the concept of digital image watermarking. The transmission of multimedia data became daily routine nowadays and it is necessary to find an efficient way to transmit through various networks. Copyright protection of multimedia data has become critical issue due to massive spreading of broadband networks, easy copying, and new developments in digital technology [18]. As a solution to this problem, digital image watermarking became very popular nowadays.

The transmission of information takes place in different forms and is used in many applications. In a large number of these applications, it is desired that the communication to be done in secret. Such secret communication includes transfer of medical data, bank transfers, corporate communications, purchases using bank cards, a large percentage of everyday emails and etc.

N.Provos and P.Honeyman [26] says that steganography is different from cryptography and watermarking, although they all have overlapping usages in the information hiding process. Steganography security hides the knowledge that there is information in the cover medium, where cryptography reveals this knowledge but encodes the data as cipher-text. Similar to steganography, watermarking is about hiding information in other image, but difference is that watermark must be somewhat resilience against attempts to remove it. This technique of information hiding can be extended to copyright protection of multimedia content. Digital watermarking and steganography techniques are used to address digital right management, protect information, and conceal secrets. Information hiding techniques provide an interesting challenge for digital forensic investigations [23].

DIGITAL STEGANOGRAPHY

The word steganography comes from the Greek word Steganos, which means covered or protected, and â€" the word graphy, which means writing or drawing. Therefore, steganography means that literally, covered writing. Steganography is the technique of hiding information such that its presence cannot be detected and a communication is happening [27]. The advantage of steganography over cryptography is that messages do not attract attention to themselves. Therefore, whereas cryptography protects the content of a message, steganography can be said to protect both messages and communicating parties [23].

2.1.1 Properties of Steganography

All the steganographic algorithms need to fulfill the following basic requirements.

Invisibility- The invisibility of steganographic algorithm is the first and foremost requirement, since the steganography lies in its ability to be unnoticed by the human eye.

Payload Capacity- Steganography aims at hidden communication and therefore requires sufficient embedding capacity.

Robustness against Stastical Attacks- Statistical steganalysis is the practice of detecting hidden information through applying statistical tests on image data.

Independent of file format-The most powerful steganographic algorithms lies in the ability to embed information in any type of file.

2.1.2 Applications of Steganography

To have secure secret communication, where strong cryptography is not possible. In military applications, where even the knowledge that two parties communicate can be of large importance.

2.2 DIGITAL IMAGE WATERMARKING

Digital image watermarking is a kind of technology that embeds copyright information into multimedia content. An effective image watermarking mainly includes watermark generation, watermark embedding, watermark detection, and watermark attack [5], [1]. Digital image watermarking provides copyright protection to image by hiding appropriate information in original image to declare rightful ownership [12]. There are four essential factors those are commonly used to determine quality of watermarking scheme. They are robustness, imperceptibility, capacity, and blindness. Robustness is a measure of immunity of watermark against attempts to image modification and manipulation like compression, filtering, rotation, scaling, noise attacks, resizing, cropping etc. Imperceptibility is the quality that the cover image should not be destroyed by the presence of watermark. Capacity includes techniques that make it possible to embed majority of information. Extraction of watermark from watermarked image without the need of original image is referred to as blind watermarking. The non-blind watermarking technique requires that the original image to exist for detection and extraction. The semi-blind watermarking scheme requires the secrete key and watermark bit sequence for extraction. Another categorization of watermarks based on the embedded data is visible or invisible [6]. Based on the robustness of the watermarks, watermarks are classified as robust watermarks, fragile watermarks and semi-fragile watermarks. Robust watermarks can resist malicious distortions, whereas fragile watermarks can easily destroyed by all image distortions and semi-fragile watermarks can be destroyed by certain type of distortions while resisting other minor changes.

The main applications of digital image watermarking include Digital Rights Management (DRM)/ Owner Identification, copyright protection and authentication. DRM can be defined as “the description, identification, trading, protecting, monitoring and tracking of all forms of usages over tangible and intangible assets [19]. It concerns the management of digital rights and enforcement of rights digitally. Copyright enables the identification of the copyright holder and thus protects rights in content distribution. It is used to prevent third party from copying or claiming the ownership of the multimedia content. Authentication in image watermarking refers to the integrity assurance of the image.

From application point of view, robust watermarks are suitable for copyright protection, because they can resist common image processing operations. On the other hand, fragile watermarks can be used to detect tampering and authenticate an image, because it is sensitive to changes. Semi-fragile watermarks are in some special cases of authentication and tamper detection.

According to the domain of watermark insertion, the watermarking techniques fall into two categories: spatial domain methods and transform domain methods. Many techniques have been proposed in the spatial domain such as LSB (Least Significant Bit) insertion method, the patch work method and the texture block coding method [6]. These techniques process the location and luminance of the image pixel directly. The LSB method has a major disadvantage that the least significant bits may be easily destroyed by lossy compression. Transform domain method based on special transformations, and process the coefficients in frequency domain to hide the data. Transform domain methods include Fast Fourier Transform (FFT),Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT),Curvelet Transform(CT), Counterlet Transform(CLT) etc. In these methods the watermark is hidden in the high and middle frequency coefficients of the cover image. The low frequency coefficients are suppressed by filtering as noise, hence watermark is not inserted in low frequency coefficients [6]. The transform domain method is more robust than the spatial domain method against compression, filtering, rotation, cropping and noise attacks.

2.2.1 Properties of Digital Image Watermarking

The efficiency of a digital image watermarking process is evaluated according to the properties of perceptual transparency, robustness, computational cost, bit rate of data embedding process, false positive rate, recovery of data with or without access to the original image, the speed of imbedding and retrieval process, the ability of embedding and retrieval module to integrate into standard encoding and decoding process etc. [27-29].

To understand watermarking methods and determine their applications, one needs to know the properties of digital image watermarking.

Robustness- of a watermark refers to its ability to withstand non-malicious distortions. The watermarking scheme should be robust to any possible signal processing operations, as long as the quality of the watermarked data preserved.

Data Payload- is the encoded message size of a watermark in an image. On the other hand, multi-bit watermarks can carry textual or pictorial information [27].

Capacity- is the amount of information in an image. If multiple watermarks are embedded into an image, then the watermarking capacity of the image is the sum of all individual watermarks data payload [27].

While the robustness of the watermarking method increases, the capacity also increases where the imperceptibility decreases. There is a tradeoff between these requirements and this tradeoff should be taken into while the watermarking method is being proposed [19].

Imperceptibility â€" is the characteristic of hiding of the watermark so that it does not degrade the visual quality of an image. The imperceptibility of the watermark is tested by peak signal to noise ratio.

Fidelity- is the visual similarity between the watermarked image and its cover image.

Security- of the watermark is the ability of the watermark to resist malicious attacks. These attacks include intentional operations of another watermark insertion, modification, removal and estimation which aim at defeating the purpose of the watermarks.

Computational cost-is the measure of computing resources required to perform watermark embedding or detection processes. It can be measured using the processing time for a given computer configuration.

There are several ways of classifying watermarking methods. One of the widely adopted classifications is based on watermark robustness. Under this classification, watermark can be grouped into 3 types:

Robust watermarks are watermarks that can resist malicious distortions.

Fragile watermarks are easily destroyed by all image distortions.

Semi-fragile watermarks can be destroyed by certain types of distortions while resisting other minor changes.

Besides watermark robustness, watermarks can also be categorized into visible and invisible types. Visible watermarks are perceptible to a viewer. On the other hand, watermarks are imperceptible and do not change the visual appearance of the images.

Depending upon the application, the properties, which are used mainly in the evaluation process, vary. For example, in the video indexing application, evaluating the robustness of a watermarking scheme to any signal processing is meaningless, since there is no case that the video passes through some signal processing operation. In the covert communication application, it is better to use a watermarking scheme that does not need the original data during the watermark detection process, if real television broadcasting is used as the communication channel, while most of the watermarking schemes in other applications need the original data during the detection process. If the application is the copyright protection, the other owner of the original data may wait for several days to insert or detect watermark, if the data is valuable for the owner. On the other hand, in a broadcast monitoring application, the speed of the watermark detection algorithm should be as fast as the speed of real time broadcasting. As a result, each watermarking application has its own requirements and the efficiency of the watermarking scheme should be evaluated according to these requirements [30].

The owner of the original data wants to prove his ownership in case of original data is copied, edited and used without permission of the owner. In the watermarking research world, this problem has been analyzed in a more detailed manner [19].

2.2.2 Applications of Digital Image Watermarks

Digital image watermarking techniques have been proposed to be implemented in many applications. Some major groups of its applications are:

Digital Rights Management(DRM)/Owner identification

DRM can be defined as the description, identification, trading, protecting, monitoring and tacking of all forms of usages over tangible and intangible assets. It concerns the management of digital rights and enforcement of rights digitally.

Copyright protection

It enables the identification of the copyright holder and thus protects the rights in content distribution. It is used to prevent third parties from copying or claiming the ownership of the digital media. Robust watermarks are embedded into an image to protect the rights of the owners. It should be possible to detect the watermark despite common image processing, geometrical distortions, image compression, and many other image manipulations. The successful detection of the watermark can positively identify the owner.

Authentication

Authentication in image watermarking refers to the integrity assurance of the image. The applications related to image authentication are the validation of cultural heritage paintings, medical records and digital artworks.

Other Applications:

There are many other applications where digital image watermarking methods have been proposed as a technology enabling tool. Some of them are:

Broadcast monitoring- watermarks embedded into advertisement sections of broadcast. It is used to track the broadcast of a particular file over a channel.

Device control- watermarks embedded into radio and television signals can be used to control features of a receiver.

Medical Applications- used in X-ray film references where they are marked with a unique ID of the patient.

Fingerprinting- to convey information about the recipient of the digital media.

Copy control- watermarks detected in a video content are used to control the functionality of a watermark complaint recorder.

Application wise robust watermarks are suitable for copyright protection, because they can resist common image processing operations. On the other hand, fragile watermarks can be used to detect tampering and authenticate an image, because it is sensitive to changes. Semi-fragile watermarks are usually applied in some special cases of authentication and tamper detection. These cases may consider lossy image compression as legitimate changes while highlighting geometrical distortions as intentional attacks.

2.2.3 Key differences between watermarking and Steganography

Digital Image Watermarking

Inserts information related to either to host signal or its owner.

Main goals are copyright protection and information authentication.

It is either visible or imperceptible.

It is for communications point-to-multiple points.

Capacity is not an important issue

Robustness is an important issue

Digital Steganography

Must not only be imperceptible but also statistically undectable.

Is for point-to-point communications.

Main goal is covert communication.

Inserts any kind of information.

Capacity is an important issue.

May or may not be robust.

2.3 DIGITAL IMAGE WATERMARKING ALGORITHMS

Digital image watermarking algorithms are classified into three categories namely spatial domain methods, feature domain methods and transform domain methods. In spatial domain methods, the watermark is embedded directly into pixel values of the original image. In feature domain methods, the watermark embedding depends upon the region, boundary and object characteristics. In transform domain methods, the watermark is embedded into the transformed coefficients of the original image. The transform methods have been found to have greater robustness, when the watermarked images tested after having been affected by different attacks.

2.3.1 Spatial Domain Techniques

Many spatial techniques are based on adding fixed amplitude pseudo noise sequences to an image. Pseudo random noise (PN) sequences are used as the spreading key when considering the host media as the noise in spread spectrum system, where the watermark is the transmitted multimedia content. Many techniques have been proposed in the spatial domain such as LSB (Least Significant Bit) insertion method, the patch work method and the texture block coding method [6]. These techniques process the location and luminance of the image pixel directly. The LSB method has a major disadvantage that the least significant bits may be easily destroyed by lossy compression. The invisibility of the watermark is achieved on the assumption that the LSB data are visually insignificant. In general, the techniques that modify the LSB of the data using a fixed magnitude PN sequence are extremely sensitive to signal processing operations and weak to watermark attacks. The contributing factor to this weakness is the fact that the watermark must be invisible. As a result, the magnitude of the embedded noise is limited by the smooth regions of the image, which most easily exhibit the embedded noise.

2.3.2 Transform Domain Techniques

Transform domain method based on special transformations, and process the coefficients in frequency domain to hide the data. Transform domain methods include Fast Fourier Transform(FFT), Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT), Curvelet Transform(CT), Counterlet Transform(CLT) etc. In these methods the watermark is hidden in the high and middle frequency coefficients of the cover image. The low frequency coefficients are suppressed by filtering as noise, hence watermark is not inserted in low frequency coefficients [7]. The transform domain method is more robust than the spatial domain method against compression, filtering, rotation, cropping and noise attack etc.

Many transform based digital image watermarking techniques have been proposed by researchers and scientists. To embed a watermark, first transform is applied on the cover image and then modifications are made to the transformed coefficients.