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

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 slection 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.