Title
Selection-Channel-Aware Rich Model for Steganalysis of Digital Images
In
Proc. of IEEE International Workshop on Information Forensics and Security
Pages
1–5
Publisher
IEEE
Year
2014
Indexed by


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 pop-
ular spatial rich model (SRM) that makes use of the se-
lection 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.
Affiliations
Offprint
- TD_WIFS_2014_Rich_Model_Adaptive.pdf (0.5 Mo)