Reliable histogram features for detecting LSB matching

This paper proposes a novel steganalyzer for detecting one of the most popular steganography, LSB matching (also known as “±1 embedding”). The histogram of difference image (the differences of adjacent pixels), which is usually a generalized Gaussian distribution centered at 0, is exploited for deriving statistical features. We have proved theoretically that the peak-value of the histogram would decrease after LSB matching embedding, while the renormalized histogram (the ratio of the histogram to the peak-value) would increase. Then we take the peak-value and the renormalized histogram as features for classification. Extensive experimental results show that the proposed steganalytic method outperforms some previous ones.