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Selection-channel-aware rich model for Steganalysis of digital images

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Abstract

From the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic selection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-the-art content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover.

Dates and versions

hal-02362227 , version 1 (13-11-2019)

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Tomas Denemark, Vahid Sedighi, Vojtech Holub, Rémi Cogranne, Jessica Fridrich. Selection-channel-aware rich model for Steganalysis of digital images. 2014 IEEE International Workshop on Information Forensics and Security (WIFS), Dec 2014, Atlanta, United States. pp.48-53, ⟨10.1109/WIFS.2014.7084302⟩. ⟨hal-02362227⟩
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