Efficient wavelet-based perceptual watermark masking for robust fingerprint image watermarking

In this study, a robust wavelet-based fingerprint image watermarking scheme using an efficient just perceptual weighting (JPW) model has been proposed. The JPW model exploits three human visual system characteristics, namely: spatial frequency sensitivity, local brightness masking and texture masking, to compute a weight for each wavelet coefficient, which is then used to control the amplitude of the inserted watermark. The idea is motivated by the fact that fingerprint images perceptually differ from natural images and a JPW model adapted to such images would further enhance the robustness of the watermarking scheme. Experimental results show that the proposed model significantly improves the performance of the conventional watermarking technique in terms of robustness while maintaining the same imperceptibility of the watermark. Finally, the proposed technique has shown a clear superiority over a number of related state-of-the-art masking techniques

DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

Feature extraction is a critical element in the design of image self-recovery and watermarking algorithms and its quality can have a big influence on the performance of these processes. The objective of the work presented in this thesis is to develop an effective methodology for feature extraction in the discrete cosine transform (DCT) domain and apply it in the design of adaptive image self-recovery and image watermarking algorithms. The methodology is to use the most significant DCT coefficients that can be at any frequency range to detect and to classify gray level patterns. In this way, gray level variations with a wider range of spatial frequencies can be looked into without increasing computational complexity and the methodology is able to distinguish gray level patterns rather than the orientations of simple edges only as in many existing DCT-based methods. The proposed image self-recovery algorithm uses the developed feature extraction methodology to detect and classify blocks that contain significant gray level variations. According to the profile of each block, the critical frequency components representing the specific gray level pattern of the block are chosen for encoding. The code lengths are made variable depending on the importance of these components in defining the block’s features, which makes the encoding of critical frequency components more precise, while keeping the total length of the reference code short. The proposed image self-recovery algorithm has resulted in remarkably shorter reference codes that are only 1/5 to 3/5 of those produced by existing methods, and consequently a superior visual quality in the embedded images. As the shorter codes contain the critical image information, the proposed algorithm has also achieved above average reconstruction quality for various tampering rates. The proposed image watermarking algorithm is computationally simple and designed for the blind extraction of the watermark. The principle of the algorithm is to embed the watermark in the locations where image data alterations are the least visible. To this end, the properties of the HVS are used to identify the gray level image features of such locations. The characteristics of the frequency components representing these features are identifying by applying the DCT-based feature extraction methodology developed in this thesis. The strength with which the watermark is embedded is made adaptive to the local gray level characteristics. Simulation results have shown that the proposed watermarking algorithm results in significantly higher visual quality in the watermarked images than that of the reported methods with a difference in PSNR of about 2.7 dB, while the embedded watermark is highly robustness against JPEG compression even at low quality factors and to some other common image processes. The good performance of the proposed image self-recovery and watermarking algorithms is an indication of the effectiveness of the developed feature extraction methodology. This methodology can be applied in a wide range of applications and it is suitable for any process where the DCT data is available

Wavelet Domain Watermark Detection and Extraction using the Vector-based Hidden Markov Model

Multimedia data piracy is a growing problem in view of the ease and simplicity provided by the internet in transmitting and receiving such data. A possible solution to preclude unauthorized duplication or distribution of digital data is watermarking. Watermarking is an identifiable piece of information that provides security against multimedia piracy. This thesis is concerned with the investigation of various image watermarking schemes in the wavelet domain using the statistical properties of the wavelet coefficients. The wavelet subband coefficients of natural images have significantly non-Gaussian and heavy-tailed features that are best described by heavy-tailed distributions. Moreover the wavelet coefficients of images have strong inter-scale and inter-orientation dependencies. In view of this, the vector-based hidden Markov model is found to be best suited to characterize the wavelet coefficients. In this thesis, this model is used to develop new digital image watermarking schemes. Additive and multiplicative watermarking schemes in the wavelet domain are developed in order to provide improved detection and extraction of the watermark. Blind watermark detectors using log-likelihood ratio test, and watermark decoders using the maximum likelihood criterion to blindly extract the embedded watermark bits from the observation data are designed. Extensive experiments are conducted throughout this thesis using a number of databases selected from a wide variety of natural images. Simulation results are presented to demonstrate the effectiveness of the proposed image watermarking scheme and their superiority over some of the state-of-the-art techniques. It is shown that in view of the use of the hidden Markov model characterize the distributions of the wavelet coefficients of images, the proposed watermarking algorithms result in higher detection and decoding rates both before and after subjecting the watermarked image to various kinds of attacks