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.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. The three main findings are as follows: (i) Scalar quantization based data hiding schemes incur a 2dB penalty from the optimal embedding strategy, which involves vector quantization of the host. (ii) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use local perceptual criteria in addition to information-theoretic guidelines. (iii) Powerful erasures and errors correcting codes provide a flexible framework that allows the data-hider freedom of choice of where to embed without requiring synchronization between encoder and decoder.

Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimality against a large class of attacks. In this paper, we consider detecting the presence of QIM hidden data, which is an important consideration when data hiding is used for covert communication, or steganography. For a given host distribution, we are able to quantify detectability compactly in terms of a parameter related to the robustness of the hiding scheme to attacks. Using detection theory we show that QIM quickly transitions from easily detectable to virtually undetectable as this parameter varies. We also obtain performance benchmarks for QIM hiding in images, indicating that a scheme designed to be robust to, say, a moderate degree of JPEG compression, should be easily detectable. While practical application of detection theory to images is difficult because of statistical variations across images, we employ supervised learning to show that standard QIM schemes for images are indeed quite easily detectable. However, it remains an open issue as to whether it is possible to devise QIM variants that are less vulnerable to steganalysis. 1.

We consider the problem of hiding images in images. In addition to the usual design constraints such as imperceptible host degradation and robustness in presence of variety of attacks, we impose the condition that the quality of the recovered signature image should be better if the attack is milder. We present a simple hybrid analogdigital hiding technique for this purpose. The signature image is compressed efficiently (using JPEG) into a sequence of bits, which is hidden using a previously proposed digital hiding scheme. The residual error between the original and compressed signature image is then hidden using an analog hiding scheme. The results show (perceptual as well as mean-square error) improvement as the attack becomes milder.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.

This paper provides a summary of our work over the past two years on robust, high-volume data hiding in images. We first present a basic framework for imageadaptive hiding, which allows selection of the coe#cients in which to hide, and employs powerful "turbo-like" erasures and errors codes in a novel manner to prevent desynchronization of encoder and decoder due to selective embedding. This coding framework provides robustness against a variety of attacks, including compression, tampering and moderate resizing. Next, we provide a joint source-channel coding scheme for image-in-image hiding, in which the quality of the recovered signature image is better if the attack is milder. This is achieved by hybrid digital-analog hiding. Finally, we present preliminary results on hiding techniques that survive printing and scanning. The techniques are devised after experimental modeling of the print-scan channel.

While microprocessor designers turn to multicore architectures to sustain performance expectations, the dramatic increase in parallelism of such architectures will put substantial demands on off-chip bandwidth and make the memory wall more significant than ever. This paper demonstrates that one profitable application of multicore processors is the execution of many similar instantiations of the same program. We identify that this model of execution is used in several practical scenarios and term it as “multi-execution.” Often, each such instance utilizes very similar data. In conventional cache hierarchies, each instance would cache its own data independently. We propose the Mergeable cache architecture that detects data similarities and merges cache blocks, resulting in substantial savings in cache storage requirements. This leads to reductions in off-chip memory accesses and overall power usage, and increases in application performance. We present cycle-accurate simulation results of 8 benchmarks (6 from SPEC2000) to demonstrate that our technique provides a scalable solution and leads to significant speedups due to reductions in main memory accesses. For 8 cores running 8 similar executions of the same application and sharing an exclusive 4-MB, 8-way L2 cache, the Mergeable cache shows a speedup in execution by 2.5× on average (ranging from 0.93 × to 6.92×), while posing an overhead of only 4.28 % on cache area and 5.21 % on power when it is used.

This paper proposes a detailed framework of storing privacy information in surveillance video as a watermark. Authorized personnel is not only removed from the surveillance video as in [1] but also embedded into the video itself, which can only be retrieved with a secrete key. A perceptual-model-based compressed domain video watermarking scheme is proposed to deal with the huge payload problem in the proposed surveillance system. A signature is also embedded into the header of the video as in [2] for authentication. Simulation results have shown that the proposed algorithm can embed all the privacy information into the video without affecting its visual quality. As a result, the proposed video surveillance system can monitor the unauthorized persons in a restricted environment, protect the privacy of the authorized persons but, at the same time, allow the privacy information to be revealed in a secure and reliable way. 1.

In this paper we attempt to quantify the “active ” steganographic capacity- the maximum rate at which data can be hidden, and correctly decoded, in a multimedia cover subject to noise/attack (hence- active), perceptual distortion criteria, and statistical steganalysis. Though work has been done in studying the capacity of data hiding as well as the rate of perfectly secure data hiding in noiseless channels, only very recently have all the constraints been considered together. In this work, we seek to provide practical estimates of steganographic capacity in natural images, undergoing realistic attacks, and using data hiding methods available today. We focus here on the capacity of an image data hiding channel characterized by the use of statistical restoration to satisfy the constraint of perfect security (under an i.i.d. assumption), as well as JPEG and JPEG-2000 attacks. Specifically we provide experimental results of the statistically secure hiding capacity on a set of several hundred images for hiding in a pre-selected band of frequencies, using the discrete cosine and wavelet transforms, where a perturbation of the quantized transform domain terms by ±1 using the quantization index modulation scheme, is considered to be perceptually transparent. Statistical security is with respect to the matching of marginal statistics of the quantized transform domain terms.