Steganalysis of LSB Matching Based on the Statistical Analysis of Empirical Matrix

In this paper, the statistical effect of embedding data on Empirical Matrix (EM) of original and differential images is investigated and a novel steganalysis method, targeted at LSB Matching is proposed. It can be mathematically proven, that embedding data in a digital image, causes its empirical matrix and, also the empirical matrixes of its differential images to smooth. Therefore, the high frequency components of an image empirical matrix are omitted due to data hiding which motivates us to extract the radial moments of EM characteristic function as discriminative features for classification. Support Vector Machine with Gaussian kernel is adopted as an appropriate classifier in classification. Experimental results show that the extracted features are highly efficient in attacking LSB Matching.