YASS is a steganographic algorithm for digital images that hides messages robustly in a key-dependent transform domain so that the stego image can be subsequently compressed and distributed as JPEG. Given the fact that state-of-the-art blind steganalysis methods of 2007, when YASS was proposed, were unable to reliably detect YASS, in this paper we steganalyze YASS using several recently proposed general-purpose steganalysis feature sets. The focus is on blind attacks that do not capitalize on any weakness of a specific implementation of the embedding algorithm. We demonstrate experimentally that twelve different settings of YASS can be reliably detected even for small embedding rates and in small images. Since none of the steganalysis feature sets is in any way targeted to the embedding of YASS, future modifications of YASS will likely be detectable by them as well.

In matrix embedding (ME) based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM) based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios (LLRs) for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data-rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.

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Our recently introduced JPEG steganographic method called Yet Another Steganographic Scheme (YASS) can resist blind steganalysis by embedding data in the discrete cosine transform (DCT) domain in randomly chosen image blocks. To maximize the embedding rate for a given image and a specified attack channel, the redundancy factor used by the repeat-accumulate (RA) code based error correction framework in YASS is optimally chosen by the encoder. An efficient method is suggested for the decoder to accurately compute this redundancy factor. We demonstrate the redundancy estimation for the quantization index modulation and matrix embedding based schemes through Sec. 2-4. The second part of this technical report (Sec. 5) discusses the steganalysis performance of YASS, using different embedding schemes, such as matrix embedding and quantization index modulation, and after using a variety of steganalysis features. Here, we shall be discussing the estimation of the RA code redundancy factor for the following 2 types of methods. For each method, the databits are RA-encoded using a suitable redundancy factor and then different embedding techniques are used to embed the code bits in the given image. • QIM-RA: use quantization index modulation (QIM) [1] to embed the RA code bits • ME-RA: use matrix embedding (ME) to embed the RA code bits We present a brief introduction into how matrix embedding operates and then also briefly describe YASS, the randomized block-based hiding framework. Matrix Embedding Example: Consider (7,3) matrix embedding, in which 3 data bits are embedded in 7 host bits. The idea is to perturb the host bits minimally so that they fall in the coset of a linear code, whose syndrome equals the data bits to be hidden. In particular, we consider the (7,4) Hamming code with parity check matrix H =