KEYWORDS: Steganalysis, Signal to noise ratio, Quantization, Network security, Databases, Detection and tracking algorithms, Statistical analysis, Data modeling, Data hiding, Interference (communication)
A blind source separation method for steganalysis of linear
additive embedding techniques is presented. The paper formulates
steganalysis as a blind source separation problem -- statistically
separate the host and secret message carrying signals. A
probabilistic model of the source distributions is defined based
on its sparsity. The problem of having fewer observations than the
number of sources is effectively handled exploiting the sparsity
and a maximum a posteriori probability (MAP) estimator is
developed to chose the best estimate of the sources. Experimental
details are provided for steganalysis of a discrete cosine
transform (DCT) domain data embedding technique.
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