Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

From the Publisher:
Steganography, a means by which two or more parties may communicate using "invisible" or "subliminal" communication, and watermarking, a means of hiding copyright data in images, are becoming necessary components of commercial multimedia applications that are subject to illegal use. This new book is the first comprehensive survey of steganography and watermarking and their application to modern communications and multimedia. 

Handbook of Information Hiding: Steganography and Watermarking helps you understand steganography, the history of this previously neglected element of cryptography, the hurdles of international law on strong cryptographic techniques, a description of possible applications, and a survey of the methods you can use to hide information in modern media. Included in this discussion is an overview of "steganalysis," methods which can be used to break steganographic communication.

This comprehensive resource also includes an introduction to and survey of watermarking methods, and discusses this method's similarities and differences to steganography. You gain a working knowledge of watermarking's pros and cons, and you learn the legal implications of watermarking and copyright issues on the Internet.

Information Hiding Techniques for Steganography and Digital Watermarking

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then low-pass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, real-time performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters.

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From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

Structured Compressed Sensing: From Theory to Applications

We propose a public key watermarking algorithm for image integrity verification. This watermark is capable of detecting any change made to an image, including changes in pixel values and image size. This watermark is important for several imaging applications, including trusted camera, legal usage of images, medical archiving of images, news reporting, commercial image transaction, and others. In each of these applications, it is important to verify that the image has not been manipulated and that the image was originated by either a specific camera or a specific user. The verification (the watermark extraction) procedure uses a public key as in public key cryptography, and hence it can be performed by any person without the secure exchange of a secret key. This is very important in many applications (e.g., trusted camera, news reporting) where the exchange of a secret key is either not possible or undesirable.

A public key watermark for image verification and authentication

We examine the question of reconstruction of signals from periodic nonuniform samples. This involves discarding samples from a uniformly sampled signal in some periodic fashion. We give a characterization of the signals that can be reconstructed at exactly the minimum rate once a nonuniform sampling pattern has been fixed. We give an implicit characterization of the reconstruction system, and a design method by which the ideal reconstruction filters may be approximated. We demonstrate that for certain spectral supports the minimum rate can be approached or achieved using reconstruction schemes of much lower complexity than those arrived at by using spectral slicing, as in earlier work. Previous work on multiband signals have typically been those for which restrictive assumptions on the sizes and positions of the bands have been made, or where the minimum rate was approached asymptotically. We show that the class of multiband signals which can be reconstructed exactly is shown to be far larger than previously considered. When approaching the minimum rate, this freedom allows us, in certain cases to have a far less complex reconstruction system.

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Minimum rate sampling and reconstruction of signals with arbitrary frequency support

A Web-Based Secure System for the Distributed Printing of Documents and Images

A technique, referred to as area-based interpolation, performs image interpolation. The system determines a curve by the pixel value at a location by taking the integral of a curve over a small area, where the size of the area is determined by the sampling size of a sampling cell. When the image is resampled with respect to a sampling cell that has a finer spacing, the system integrates the polynomial using a finer integration area. In accordance with the invention, the relation between the reintegrated, resampled high resolution image and the low resolution image is a function of an up-sampler, followed by a linear filter. The coefficients of the filter are independent of the data, but are dependent on the family of curves used. If the system models by, for example, a third degree or fourth degree polynomial everywhere in the image, then that model determines the number of coefficients that are in the filter. Once the up-sampling factor and degree of the polynomial are determined, the filter coefficients are fixed, and can be calculated by solving a set of linear equations. It is not necessary to determine these values on an image-by-image basis. The system calculates the filter once and the consistency constraint is then satisfied for all image scaling.

Area based interpolation for image scaling

In one embodiment, a method for controlling artifacts in an image due to a presence of flicker in ambient light is provided. The method comprises determining a power frequency for the ambient light; determining a magnitude and phase of flicker fluctuation in the image due to the flicker; and adjusting the image for the flicker fluctuation based on the power frequency, magnitude, and phase.

Ping Wah Wong

Papers

A public key watermark for image verification and authentication

Minimum rate sampling and reconstruction of signals with arbitrary frequency support

A Web-Based Secure System for the Distributed Printing of Documents and Images

Area based interpolation for image scaling

Method and system to reduce flicker artifacts in captured images