www.cs.dartmouth.edu/~{lsw,farid} Abstract. Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images. 1

Abstract. In this paper, we introduce a new feature-based steganalytic method for JPEG images and use it as a benchmark for comparing JPEG steganographic algorithms and evaluating their embedding mechanisms. The detection method is a linear classifier trained on feature vectors corresponding to cover and stego images. In contrast to previous blind approaches, the features are calculated as an L 1 norm of the difference between a specific macroscopic functional calculated from the stego image and the same functional obtained from a decompressed, cropped, and recompressed stego image. The functionals are built from marginal and joint statistics of DCT coefficients. Because the features are calculated directly from DCT coefficients, conclusions can be drawn about the impact of embedding modifications on detectability. Three different steganographic paradigms are tested and compared. Experimental results reveal new facts about current steganographic methods for JPEGs and new design principles for more secure JPEG steganography. 1

We describe a statistical model for natural images that is built upon a multi-scale wavelet decomposition. The model consists of first- and higher-order statistics that capture certain statistical regularities of natural images. We show how this model can be useful in several digital forensic applications, specifically in detecting various types of digital tampering.

This article discusses existing steganographic systems and presents recent research in detecting them via statistical steganalysis. Other surveys focus on the general usage of information hiding and watermarking or else provide an overview of detection algorithms

Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eye. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.

Techniques for information hiding (steganography) are becoming increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages is also becoming considerably more difficult. We describe a universal approach to steganalysis for detecting the presence of hidden messages embedded within digital images. We show that, within multi-scale, multi-orientation image decompositions (e.g., wavelets), first- and higher-order magnitude and phase statistics are relatively consistent across a broad range of images, but are disturbed by the presence of embedded hidden messages. We show the efficacy of our approach on a large collection of images, and on eight different steganographic embedding algorithms.

In this paper, we show that the communication channel known as writing in memory with defective cells [1][2] is a relevant information-theoretical model for a specific case of passive warden steganography when the sender embeds a secret message into a subset C of the cover object X without sharing C with the recipient. The set C, also called the selection channel, could be arbitrary, determined by the sender from the cover object using a deterministic, pseudo-random, or a truly random process. We call this steganography “writing on wet paper ” and realize it using a simple variable-rate random linear code that gives the sender a convenient flexibility and control over the embedding process and is thus suitable for practical implementation. The importance of the wet paper scenario for covert communication is discussed within the context of adaptive steganography and perturbed quantization steganography [3]. Heuristic arguments supported by tests using blind steganalysis [4] indicate that the wet paper steganography provides improved steganographic security and is less vulnerable to steganalytic attacks compared to existing methods with shared selection channels.

The goal of this paper is to determine the steganographic capacity of JPEG images (the largest payload that can be undetectably embedded) with respect to current best steganalytic methods. Additionally, by testing selected steganographic algorithms we evaluate the influence of specific design elements and principles, such as the choice of the JPEG compressor, matrix embedding, adaptive content-dependent selection channels, and minimal distortion steganography using side information at the sender. From our experiments, we conclude that the average steganographic capacity of grayscale JPEG images with quality factor 70 is approximately 0.05 bits per non-zero AC DCT coefficient.

In this paper, we describe new methodology for developing steganalytic methods for JPEG images. The proposed framework can be applied to virtually all current methods for JPEGs including OutGuess, F5, and J-Steg. It also enables accurate estimation of the length of the embedded secret message. The methodology is demonstrated on OutGuess 0.2.

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.