Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector

A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

On the data hiding theory and multimedia content security applications

This dissertation is a comprehensive study of digital steganography for multimedia content protection. With the increasing development of Internet technology, protection and enforcement of multimedia property rights has become a great concern to multimedia authors and distributors. Watermarking technologies provide a possible solution for this problem. The dissertation first briefly introduces the current watermarking schemes, including their applications in video,, image and audio. Most available embedding schemes are based on direct Spread Sequence (SS) modulation. A small value pseudo random signature sequence is embedded into the host signal and the information is extracted via correlation. The correlation detection problem is discussed at the beginning. It is concluded that the correlator is not optimum in oblivious detection. The Maximum Likelihood detector is derived and some feasible suboptimal detectors are also analyzed. Through the calculation of extraction Bit Error Rate (BER), it is revealed that the SS scheme is not very efficient due to its poor host noise suppression. The watermark domain selection problem is addressed subsequently. Some implications on hiding capacity and reliability are also studied. The last topic in SS modulation scheme is the sequence selection. The relationship between sequence bandwidth and synchronization requirement is detailed in the work. It is demonstrated that the white sequence commonly used in watermarking may not really boost watermark security. To address the host noise suppression problem, the hidden communication is modeled as a general hypothesis testing problem and a set partitioning scheme is proposed. Simulation studies and mathematical analysis confirm that it outperforms the SS schemes in host noise suppression. The proposed scheme demonstrates improvement over the existing embedding schemes. Data hiding in audio signals are explored next. The audio data hiding is believed a more challenging task due to the human sensitivity to audio artifacts and advanced feature of current compression techniques. The human psychoacoustic model and human music understanding are also covered in the work. Then as a typical audio perceptual compression scheme, the popular MP3 compression is visited in some length. Several schemes, amplitude modulation, phase modulation and noise substitution are presented together with some experimental results. As a case study, a music bitstream encryption scheme is proposed. In all these applications, human psychoacoustic model plays a very important role. A more advanced audio analysis model is introduced to reveal implications on music understanding. In the last part, conclusions and future research are presented

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

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work

Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks

Digital Multimedia Forensics and Anti-Forensics

As the use of digital multimedia content such as images and video has increased, so has the means and the incentive to create digital forgeries. Presently, powerful editing software allows forgers to create perceptually convincing digital forgeries. Accordingly, there is a great need for techniques capable of authenticating digital multimedia content. In response to this, researchers have begun developing digital forensic techniques capable of identifying digital forgeries. These forensic techniques operate by detecting imperceptible traces left by editing operations in digital multimedia content. In this dissertation, we propose several new digital forensic techniques to detect evidence of editing in digital multimedia content. We begin by identifying the fingerprints left by pixel value mappings and show how these can be used to detect the use of contrast enhancement in images. We use these fingerprints to perform a number of additional forensic tasks such as identifying cut-and-paste forgeries, detecting the addition of noise to previously JPEG compressed images, and estimating the contrast enhancement mapping used to alter an image. Additionally, we consider the problem of multimedia security from the forger's point of view. We demonstrate that an intelligent forger can design anti-forensic operations to hide editing fingerprints and fool forensic techniques. We propose an anti-forensic technique to remove compression fingerprints from digital images and show that this technique can be used to fool several state-of-the-art forensic algorithms. We examine the problem of detecting frame deletion in digital video and develop both a technique to detect frame deletion and an anti-forensic technique to hide frame deletion fingerprints. We show that this anti-forensic operation leaves behind fingerprints of its own and propose a technique to detect the use of frame deletion anti-forensics. The ability of a forensic investigator to detect both editing and the use of anti-forensics results in a dynamic interplay between the forger and forensic investigator. We use develop a game theoretic framework to analyze this interplay and identify the set of actions that each party will rationally choose. Additionally, we show that anti-forensics can be used protect against reverse engineering. To demonstrate this, we propose an anti-forensic module that can be integrated into digital cameras to protect color interpolation methods

Robust digital image watermarking algorithms for copyright protection

Digital watermarking has been proposed as a solution to the problem of resolving copyright ownership of multimedia data (image, audio, video). The work presented in this thesis is concerned with the design of robust digital image watermarking algorithms for copyright protection. Firstly, an overview of the watermarking system, applications of watermarks as well as the survey of current watermarking algorithms and attacks, are given. Further, the implementation of feature point detectors in the field of watermarking is introduced. A new class of scale invariant feature point detectors is investigated and it is showed that they have excellent performances required for watermarking. The robustness of the watermark on geometrical distortions is very important issue in watermarking. In order to detect the parameters of undergone affine transformation, we propose an image registration technique which is based on use of the scale invariant feature point detector. Another proposed technique for watermark synchronization is also based on use of scale invariant feature point detector. This technique does not use the original image to determine the parameters of affine transformation which include rotation and scaling. It is experimentally confirmed that this technique gives excellent results under tested geometrical distortions. In the thesis, two different watermarking algorithms are proposed in the wavelet domain. The first algorithm belongs to the class of additive watermarking algorithms which requires the presence of original image for watermark detection. Using this algorithm the influence of different error correction codes on the watermark robustness is investigated. The second algorithm does not require the original image for watermark detection. The robustness of this algorithm is tested on various filtering and compression attacks. This algorithm is successfully combined with the aforementioned synchronization technique in order to achieve the robustness on geometrical attacks. The last watermarking algorithm presented in the thesis is developed in complex wavelet domain. The complex wavelet transform is described and its advantages over the conventional discrete wavelet transform are highlighted. The robustness of the proposed algorithm was tested on different class of attacks. Finally, in the thesis the conclusion is given and the main future research directions are suggested