We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing information-embedding rate, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is "provably good" against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular, it achieves provably better rate distortion--robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applications.

The growth of new imaging technologies has created a need for techniques that can be used for copyright protection of digital images. Copyright protection involves the authentication of image content and/or ownership and can be used to identify illegal copies of a (possibly forged) image. One approach for copyright protection is to introduce an invisible signal known as a digital watermark in the image. In this paper, we describe digital image watermarking techniques, known as perceptually based watermarks, that are designed to exploit aspects of the human visual system. In the most general sense, any watermarking technique that attempts to incorporate an invisible mark into an image is perceptually based. However, in order to provide transparency (invisibility of the watermark) and robustness to attack, more sophisticated use of perceptual information in the watermarking process is required. Several techniques have been introduced that incorporate a simple visual model in the marking procedure. Such techniques usually take advantage of frequency selectivity and weighing to provide some perceptual criteria in the watermarking process. Even more elaborate visual models are used to develop schemes that not only take advantage of frequency

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Quantization index modulation (QIM) methods, a class of digital watermarking and information embedding methods, achievevery efficient trade-offs among the amount of embedded information (rate), the amount of embedding-induced distortion to the host signal, and the robustness to intentional and unintentional attacks. For example, we show that against independent additive Gaussian attacks, which are good models for at least some types of uninformed and unintentional attacks, QIM methods exist that achieve the best possible rate-distortion-robustness trade-offs (i.e., capacity) asymptotically at high rates and achieve performance within a few dB of capacity at all finite rates. Furthermore, low-complexity realizations of QIM methods, such as so-called dither modulation, have also been shown to achieve favorable rate-distortion-robustness trade-offs. We further develop preprocessing and postprocessing techniques that enable QIM to fully achieve capacity, not only against Gaussian attacks but also ag...

Copyright notification and enforcement, authentication, covert communication, and hybrid transmission are examples of emerging multimedia applications for digital watermarking methods, methods for embedding one signal (e.g., the digital watermark) within another “host ” signal to form a third, “composite ” signal. The embedding is designed to achieve efficient trade-offs among the three conflicting goals of maximizing information-embedding rate, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. Quantization indexmodulation (QIM) methods are a class of watermarking methods that achieve provably good rate-distortion-robustness performance. Indeed, QIM methods exist that achieve performance within a few dB of capacity in the case of a (possibly colored) Gaussian host signal and an additive (possibly colored) Gaussian noise channel. Also, QIM methods can achieve capacity with a type of postprocessing called distortion compensation. This capacity is independent of host signal statistics, and thus, contrary to popular belief, the information-embedding capacity when the host signal is not available at the decoder is the same as the case when the host signal is available at the decoder. A low-complexity realization of QIM called dither modulation has previously been proven to be better than both linear methods of spread spectrum and nonlinear methods of low-bit(s) modulation against square-error distortionconstrained intentional attacks. We introduce a new form of dither modulation called spread-transform dither modulation that retains these favorable performance characteristics while achieving better performance against other attacks such as JPEG compression.

This tutorial paper reviews the theory and design of codes for hiding or embedding information in signals such as images, video, audio, graphics, and text. Such codes have also been called watermarking codes; they can be used in a variety of applications, including copyright protection for digital media, content authentication, media forensics, data binding, and covert communications. Some of these applications imply the presence of an adversary attempting to disrupt the transmission of information to the receiver; other applications involve a noisy, generally unknown, communication channel. Our focus is on the mathematical models, fundamental principles, and code design techniques that are applicable to data hiding. The approach draws from basic concepts in information theory, coding theory, game theory, and signal processing, and is illustrated with applications to the problem of hiding data in images. Keywords—Coding theory, data hiding, game theory, image processing, information theory, security, signal processing, watermarking. I.

We present a novel technique to characterize and index violent scenes in general TV drama and movies. Our goal is to identify violent signatures and localize violent events within a movie to support #high-level" video indexing. In particular, we exploit multiple #audiovisual " signatures to create a perceptual relation for conceptually meaningful violent scene identi#cation. Potential applications are automatic blocking of violence in movies watched by children, hiding violence using data hiding or information #ltering and genre classi#- cation of digital video database.

We describe a data hiding technique which uses noiseresilient channel codes based on multidimensional lattices. A trade-off between the quantity of hidden data and the quality of the watermarked image is achieved by varying the number of quantization levels for the signature and a scale factor for data embedding. Experimental results show that the watermarked image is transparent to embedding for large amounts of hidden data, and the quality of the extracted signature is high even when the watermarked image is subjected to up to 75% Wavelet compression and 85% JPEG lossy compression. These results can be combined with a private key-based scheme to make unauthorized retrieval practically impossible, even with the knowledge of the algorithm. Keywords: digital watermarking, data hiding, copyright protection, multidimensional lattice quantizer 1 Introduction Motivated by the overwhelming urge for Internet data security, digital watermarking has recently emerged as an important area of r...

: Digital data hiding is a technology being developed for multimedia services, where non-trivial amounts of signature data is invisibly hidden inside a host data source by the owner before the latter is freely distributed. Only those authorized can recover the hidden data from the host, even after the latter has undergone standard transformations such as compression. In this work we adopt a quantitative source and channel coding approach to hiding large amounts of compressible signature data inside the raw host. The signature data is source coded by vector quantization, and the indices are embedded in the host by perturbing it using orthogonal transform domain vector perturbations. The transform coefficients of the parent data are grouped into vectors, and the vectors are perturbed using noise-resilient channel codes derived from multidimensional lattices. The perturbations are constrained by a maximum allowable mean-squared error that can be introduced in the host. The generic approac...

Digital data hiding is a technology being developed for multimedia services, where significant amounts of secure data is invisibly hidden inside a host data source by the owner, for retrieval only by those authorized. The hidden data should be recoverable even after the host has undergone standard transformations, such as compression. In this paper, we present a source and channel coding framework for data hiding, allowing any tradeoff between the visibility of distortions introduced, the amount of data embedded, and the degree of robustness to noise. The secure data is source coded by vector quantization, and the indices obtained in the process are embedded in the host video using orthogonal transform domain vector perturbations. Transform coefficients of the host are grouped into vectors and perturbed using noise-resilient channel codes derived from multidimensional lattices. The perturbations are constrained by a maximum allowable mean-squared error that can be introduced in the host. Channel-optimized can be used for increased robustness to noise. The generic approach is readily adapted to make retrieval possible for applications where the original host is not available to the retriever. The secure data in our implementations are low spatial and temporal resolution video, and sampled speech, while the host data is QCIF video. The host video with the embedded data is H.263 compressed, before attempting retrieval of the hidden video and speech from the reconstructed video. The quality of the extracted video and speech is shown for varying compression ratios of the host video.