Abstract — Wet paper codes were previously proposed as a tool for construction of steganographic schemes with arbitrary (non-shared) selection channels. In this paper, we propose a new approach to wet paper codes using random linear codes of small codimension that at the same time improves the embedding efficiency (number of random message bits embedded per embedding change). Practical algorithms are given and their performance is evaluated experimentally and compared to theoretically achievable bounds. An approximate formula for the embedding efficiency of the proposed scheme is derived. The proposed coding method can be modularly combined with most steganographic schemes to improve their security. Index Terms — Steganography, covering codes, embedding efficiency, wet paper codes, matrix embedding, selection channel. I.

The contribution of this paper is two-fold. First, we describe an improved version of a blind steganalysis method previously proposed by Holotyak et al. 1 and compare it to current state-of-the-art blind steganalyzers. The features for the blind classifier are calculated in the wavelet domain as higher-order absolute moments of the noise residual. This method clearly shows the benefit of calculating the features from the noise residual because it increases the features’ sensitivity to embedding, which leads to improved detection results. Second, using this detection engine, we attempt to answer some fundamental questions, such as "how much can we improve the reliability of steganalysis given certain a priori side-information about the image source? " Moreover, we experimentally compare the security of three steganographic schemes for images stored in a raster format – (1) pseudo-random ±1 embedding using ternary matrix embedding, (2) spatially adaptive ternary ±1 embedding, and (3) perturbed quantization while converting a 16-bit per channel image to an 8-bit gray scale image. 1.

In this paper, we present a framework for the design of steganographic schemes that can provide provable security by achieving zero Kullback-Leibler divergence between the cover and the stego signal distributions, while hiding at high rates. The approach is to reserve a number of host symbols for statistical restoration: host statistics perturbed by data embedding are restored by suitably modifying the symbols from the reserved set. A dynamic embedding approach is proposed, which avoids hiding in low probability regions of the host distribution. The framework is applied to design practical schemes for image steganography, which are evaluated using supervised learning on a set of about 1000 natural images. For the presented JPEG steganography scheme, it is seen that the detector is indeed reduced to random guessing. 1.

Abstract. A new, simple, approach for active steganography is proposed in this paper that can successfully resist recent blind steganalysis methods, in addition to surviving distortion constrained attacks. We present Yet Another Steganographic Scheme (YASS), a method based on embedding data in randomized locations so as to disable the selfcalibration process (such as, by cropping a few pixel rows and/or columns to estimate the cover image features) popularly used by blind steganalysis schemes. The errors induced in the embedded data due to the fact that the stego signal must be advertised in a specific format such as JPEG, are dealt with by the use of erasure and error correcting codes. For the presented JPEG steganograhic scheme, it is shown that the detection rates of recent blind steganalysis schemes are close to random guessing, thus confirming the practical applicability of the proposed technique. We also note that the presented steganography framework, of hiding in randomized locations and using a coding framework to deal with errors, is quite simple yet very generalizable. Key words: data hiding, error correcting codes, steganalysis, steganography, supervised learning. 1

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 investigates systems that steganographically embed information in the “noise ” created by automatic translation of natural language documents. The main thrust of the work focuses on two problems- generation of plausible steganographic texts, and avoiding transmission of the original source for stego objects. Because the inherent redundancy of natural language creates plenty of room for variation in translation, machine translation is ideal for steganographic applications. We describe the design and implementation of a scheme for hiding information in translated natural language text and present experimental results using the implemented system. While the initial work in this vein required the presence of both the source and the translation, the system detailed in this paper requires only the translated text for recovering the hidden message, increasing security and improving resource usage. These improvements occur not only because the source text is no longer available to the adversary, but also because a broader repertoire of defenses (such as mixing human and machine translation) can now be used. ∗ Preliminary and shorter versions of this work appeared in [21, 38]. 1 1

We present further extensions of yet another steganographic scheme (YASS), a method based on embedding data in randomized locations so as to resist blind steganalysis. YASS is a JPEG steganographic technique that hides data in the discrete cosing transform (DCT) coefficients of randomly chosen image blocks. Continuing to focus on JPEG image steganography, we present, in this paper, a further study on YASS with the goal of improving the rate of embedding. Following are the two main improvements presented in this paper: (i) a method that randomizes the quantization matrix used on the transform domain coefficients, and (ii) an iterative hiding method that utilizes the fact that the JPEG “attack ” that causes errors in the hidden bits is actually known to the encoder. We show that using both these approaches, the embedding rate can be increased while maintaining the same level of undetectability (as the original YASS scheme). Moreover, for the same embedding rate, the proposed steganographic schemes are more undetectable than the popular matrix embedding based F5 scheme, using features proposed by Pevny and Fridrich for blind steganalysis.

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.

This paper describes the design and implementation of a scheme for hiding information in translated natural language text, and presents experimental results using the implemented system. Unlike the previous work, which required the presence of both the source and the translation, the protocol presented in this paper requires only the translated text for recovering the hidden message. This is a significant improvement, as transmitting the source text was both wasteful of resources and less secure. The security of the system is now improved not only because the source text is no longer available to the adversary, but also because a broader repertoire of defenses (such as mixing human and machine translation) can now be used.

In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact of making an embedding change at that element (single-letter distortion). The problem is to embed a given payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past. Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners, we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.