A Digital Image Watermarking Computer Science Essay

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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.

2.1 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 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.

Nagaraj V. Dharwadkar et al [6], proposed a non-blind watermarking scheme for color images in RGB space using DWT-SVD. In this method, the watermark is embedded into cover image in RGB space. The combinations of discrete wavelet transform and singular value decomposition of blue channel is used to embed watermark. The singular values of different sub band coefficients of blue channel are modified using different scaling factors to embed the singular values of the watermark. The copy of the watermark is embedded into four sub band coefficients which is very difficult to remove or destroy [6].

Yanhong Zhang [7] proposed a blind watermark embedding/extracting algorithm using RBF neural network. In this method the DWT is used to overcome the blocking phenomenon problems in DCT. First, the original image is 4-scale level DWT transformed, and decided the watermarking strength according to HVS. When embedding watermark, a secret key is used to determine the watermark beginning location, and after that, embed and extract the watermark by using the trained RBF [7].

He Xu, Chang Shujuan [10], proposed an adaptive image watermarking algorithm based on neural network. In this method, the watermarking signal is embedded in high frequency, which is in the lower frequency of original image by DWT joined with DCT. The ability of attracting is improved by pretreatment and retreatment of image scrambling and Hopfield network [10].

Samesh Oueslati et al, [13] proposed an adaptive image watermarking scheme based on Multi-Layer Feed forward (MLF) neural networks. In this method the host image is first decomposed into non-overlapping 8x8 blocks, and the DCT process is performed for each block. Coefficients are then selected for watermark insertion. Human Visual System (HVS) is adopted to further ensure the watermark invisibility. Then the luminance sensitivity, frequency sensitivity, texture sensitivity and entropy sensitivity are computed and used to as the inputs of the NNS. In this paper, neural networks are used to automatically control and maximum imageâ€"adaptive strength watermark [13].

Cheng-Ri Piao et al, [16] proposed a blind watermarking algorithm based on HVS and RBF neural network for digital images. In this method, RBF is implemented while embedding and extracting watermark. The human visual system model is used to determine the watermark insertion strength. The inserted watermark is a random sequence. The secret key determines the beginning position of the image where the watermark is embedded. This process prevents possible pirates from removing the watermark easily [16].

Nizar Sakr et al, [20] proposed an adaptive wavelet-based watermarking algorithm that is based on the model of a HVS and a Fuzzy Inference System (FIS).In this method; Sugeno-type fuzzy model is exploited in order to determine a valid approximation of the quantization step of each DWT coefficient. Furthermore, the HVS properties are modeled using biorthogonal wavelets to improve watermark robustness and imperceptibility [20].

Charu Agarwal et al, [24] proposed digital image watermarking in DCT domain using fuzzy inference system. In this method, Human Visual System (HVS) characteristics are modeled using a Fuzzy Inference System (FIS) for robust image watermarking. The fuzzy input variables corresponding to luminance sensitivity, edge sensitivity computed using threshold and contrast sensitivity computed using variance are fed to a FIS driven by ten fuzzy inference rules. The FIS produces a single output weighting factor which is used to embed a randomly generated normalized watermark with in the host image in the DCT domain. The signed image has good perceptual quality and is subject to stir mark image processing attacks. The high computed value of PSNR indicates robustness of the embedding algorithm. The watermark is extracted from the signed image using famous Cox’s algorithm [24].

Samesh Oueslati et al [25], proposed a fuzzy watermarking system using the wavelet technique for medical images. In this method, an adaptive watermarking algorithm performed in the wavelet domain is proposed which exploits a human visual system (HVS) and a fuzzy inference system (FIS). HVS is adopted to further ensure the watermark invisibility. The FIS is utilized to compute the optimum watermark weighting function that would enable the embedding of the maximum energy and imperceptible watermark. For the purpose of security and robustness, a watermark sequence is embedded by selectively modifying the middle- frequency parts of the image [25].

Ming- Shing Hsieh [26] proposed perceptual copyright protection using multi-resolution wavelet- based watermarking and fuzzy logic. In this method, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This technique is based on utilizing a context model and fuzzy inference filter by embedding the watermarks in the larger entropy coefficients of coarser DWT sub bands [26].

Soheila Kiani et al [27], proposed Fractal based digital image watermarking using fuzzy C-mean clustering. In this method a new watermarking method is to embed a binary watermark in to an image. The proposed method uses a special type of fractal coding that its parameters are contrast scaling the mean of rage block. Also, it utilizes the fuzzy C-mean clustering to address the watermark bits [27].

2.3 RESEARCH OBJECTIVES

The objectives of this research work are as follows:

To explore digital image watermarking techniques using Back Propagation Neural Network and Dynamic Fuzzy Inference System in Discrete Wavelet Transform domain.

To develop watermarking techniques, which are imperceptible for unauthorized user, without affecting the original image quality.

To develop blind watermarking techniques so that the watermark can be detected without the original image.

To develop watermark techniques, which are robust against cropping, salt&pepper noise, rotation, JPEG compression etc., and having supremacy over existing watermarking methods.

PROBLEM STATEMENT

From the literature review, it is apparent that the digital image watermarking can be achieved by using either embedding the watermark directly into the image pixels of the cover image or into the transformed coefficients of the cover image. There are several requirements that the embedding method has yet to satisfy. Creating the robust and blind digital image watermarking methods is still a challenging task for researchers. These algorithms are robust against some attacks but not against most of them. Also, some of the current methods are designed to suit only specific application, which limits their wide spread use. Moreover, there are drawbacks in the existing algorithms associated with the watermark-embedding domain. These drawbacks vary from system to system. Watermarking schemes that modify the LSB of the data using a fixed magnitude PN sequence are highly sensitive to signal processing operations and geometric manipulations. This will limit their use in large number of applications.

To enable copyright protection and authentication, robust digital watermark can be embedded into multimedia contents imperceptibly. However, geometric distortions pose a significant threat to robust image watermarking because it can desynchronize the watermark information while preserving the visual quality. To overcome this, the robust digital image watermarking scheme using Back Propagation Neural Network in DWT domain is proposed, in which the geometrical effects such as cropping and rotation are minimized. 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. Owning to the use of neural network, we can extract watermark without the original signal and thus reduce the limit in practical applications. The correlation coefficient is further improved by using Dynamic Fuzzy Inference System. The primary novelty of this scheme is that the 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 biorthogonal wavelets to improve watermark robustness and imperceptibility. Finally, the results of BPNN and DFIS methods are compared.

CHAPTER SUMMARY

This chapter presented an overview of digital image watermarking. A survey is made on digital image watermarking and their limitations are also presented. Different domains of watermarking are explained in the next chapter.

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