An analysis of steganographic systems subject to the following perfect undetectability condition is presented in this paper. Following embedding of the message into the covertext, the resulting stegotext is required to have exactly the same probability distribution as the covertext. Then no statistical test can reliably detect the presence of the hidden message. We refer to such steganographic schemes as perfectly secure. A few such schemes have been proposed in recent literature, but they have vanishing rate. We prove that communication performance can potentially be vastly improved; specifically, we construct perfectly secure steganographic codes from public watermarking codes using binning methods and randomization of the code over an invariant group associated with the covertext distribution (e.g., a permutation group in the case of independently and identically distributed covertext). We derive (positive) capacity and random-coding exponents for perfectly secure steganographic systems. In our steganographic problem, communication may be disrupted by an active warden, modelled here by a compound discrete memoryless channel. The transmitter and warden are subject to distortion constraints. In our basic setup, the covertext samples are independently and identically distributed (i.i.d.) over a finite alphabet. A secret key is shared by the encoder and decoder and provides the desired perfect security via randomization of the steganographic code. We address the potential loss in communication performance due to the perfect security requirement. We

This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (non-binary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the non-binary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndromecoding scheme based on dual convolutional codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in steganographic applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.

The predictive-coding-based (PCB) steganography can embed a large amount of bits into the code stream of lossless compression with high imperceptibility. However, based on two elaborately chosen statistical features, the proposed steganalytic method can easily find the presence of a secret message with small error probability. To enhance the scheme’s security, a modified one is proposed, which preserves the prediction errors ’ distribution by choosing the optimum adjustment parameter. Experimental results prove that the modified scheme can provide near-perfect security in Cachin’s definition and defeat the steganalytic method proposed by ourselves. Key words:

The problem of joint universal source coding and identification is considered in the setting of fixed-rate lossy coding of continuous-alphabet memoryless sources. For a wide class of bounded distortion measures, it is shown that any compactly parametrized family of R d-valued i.i.d. sources with absolutely continuous distributions satisfying appropriate smoothness and Vapnik–Chervonenkis learnability conditions, admits a joint scheme for universal lossy block coding and parameter estimation, such that when the block length n tends to infinity, the overhead per-letter rate and the distortion redundancies converge to zero as O(n −1 log n) and O ( � n −1 log n), respectively. Moreover, the active source can be determined at the decoder up to a ball of radius O ( � n −1 log n) in variational distance, asymptotically almost surely. The system has finite memory length equal to the block length, and can be thought of as blockwise application of a time-invariant nonlinear filter with initial conditions determined from the previous block. Comparisons are presented with several existing schemes for universal vector quantization, which do not include parameter estimation explicitly, and an extension to unbounded distortion measures is outlined. Finally, finite mixture classes and exponential families are given as explicit examples of parametric sources admitting joint universal compression and modeling schemes of the kind studied here. Keywords: Learning, minimum-distance density estimation, two-stage codes, universal vector quantization, Vapnik– Chervonenkis dimension. I.

Abstract. Image watermarking is a mapping from watermark message to a set of image counterparts, where every version conveys the same meaning with the original image. Since textures that present single perceptual meaning have certain diversity, an intuitive idea of watermarking is to replace the texture region of an image with a similar-looking synthetic texture containing the watermark. We propose a spread-spectrum watermarking scheme by integrating existent work on texture extraction, segmentation and synthesis, and obtain suggestive results, including (1) the synthetic watermarks can resist adaptive Wiener filtering attack due to its power spectrum similar with the original image; (2) if using the spread-spectrum carrier which is designed elaborately according to the subspace spanned by the textures, hiding capacity can be improved by 20%, while effective hiding capacity under Wiener filtering attack can be doubled; (3) the proposed watermarking scheme also enlighten a sophisticate strategy for watermark attack. 1

While binning is a fundamental approach to blind data embedding and watermarking, an attacker may devise various strategies to reduce the effectiveness of practical binning schemes. The problem analyzed in this paper is design of worst-case noise distributions against L-dimensional lattice Quantization Index Modulation (QIM) watermarking codes. The cost functions considered are (1) probability of error of the maximum-likelihood decoder, and (2) the more tractable Bhattacharyya upper bound on error probability, which is tight at low embedding rates. Both problems are addressed under the following constraints on the attacker’s strategy: the noise is independent of the marked signal, blockwise memoryless with block length L, and may not exceed a specified quadratic-distortion level. The embedder’s quadratic distortion is limited as well. Three strategies are considered for the embedder: optimization of the lattice inflation parameter (aka Costa parameter), dithering, and randomized lattice rotation. Critical in this analysis are the symmetry properties of QIM nested lattices and convexity properties of probability of error and related functionals of the noise distribution. We derive the minmax optimal embedding and attack strategies and obtain explicit solutions as well as numerical solutions for the worst-case noise. The role of the attacker’s memory is investigated; in particular, we demonstrate the remarkable effectiveness of impulsive-noise attacks as L increases. The formulation proposed in this paper is also used to evaluate the capacity of lattice QIM under worst-noise conditions.

Abstract. We propose two efficient information hiding algorithms in the least significant bits of JPEG coefficients of images. Our algorithms embed information by modifying JPEG coefficients in such a way that the introduced distortion is minimized. We derive the expected value of the additional error due to distortion as a function of the message length and the probability distribution of the JPEG quantization errors of cover images. We have implemented our methods in Java and performed the extensive experiments with them. The experiments have shown that our theoretical predictions agree closely with the actual introduced distortions.

In this paper we describe a process whereby the magnitude of either one or two frequency components of a signal is modified in order that it may be used to encode a hidden message within a signal in such a way as the casual observer would have no way of noticing the presence of a hidden message. Previous work has used filtering and signal addition to achieve the same goals. The current work improves on this by using a recent super-resolution component-identification technique to isolate the components to modify, limiting the impact on the quality of the signal.

The proliferation of multimedia and the advent of the Internet and other public networks have created many new applications of information hiding in multimedia security and forensics. This dissertation focuses on two of these application scenarios: steganography (and its counter problem, steganalysis), and fingerprinting. First, from a detection-theoretic perspective, we quantify the detectability of commonly used information-hiding techniques such as spread spectrum and distortion-compensated quantization index modulation, and also the detectability of block-based steganography. We devise a practical steganalysis method that exploits the peculiar block structure of block-DCT image steganography. To cope with the twin difficulties of unknown image statistics and unknown steganographic codes, we explore image steganalysis based on supervised learning and build an optimized classifier that outperforms previously proposed image steganalysis methods. Then, from an information-theoretic perspective, we derive the capacity and random-coding error exponent of perfectly secure steganography and public fingerprinting. For both games, a randomized stacked-binning scheme and a matched maximum penalized mutual information decoder are used to achieve capacity and to realize a random-coding error exponent that is strictly positive at all rates below capacity.

Steganography is the art of writing hidden messages in such a way that no one; apart from the sender and intended recipient even understand there is a hidden message. Steganography includes the concealment of information within computer files. One of the most common methods of implementation is Least Significant Bit Insertion, in which the least significant bit of every byte is altered to form the bit-string representing the embedded file. Altering the LSB will only cause minor changes in color. While this technique works well for 24-bit color image files, steganography has not been as successful when using an 8-bit color image file, due to limitations in color variations and the use of a color table. Color table is organized as- the first three bytes correspond to RGB components and the last byte is reserved or unused. The proposed technique is to generate the image from a 24-bit bitmap to an 8-bit bitmap using color quantization resulted in minor variations in the image, which are barely noticeable to the human eye. However, these slight variations aid in hiding the data.