Blind Source Separation for Steganalytic Secret Message Estimation

摘要

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.