Data hiding domain classification for blind image steganalysis

A statistical feature-based scheme is proposed to identify the data hiding domain of an embedded signal in this research. Two phenomena are observed for images before and after data hiding. First, the gradient energy increases as the continuity of gray levels between adjacent pixels is disturbed by the embedded signal. Second, the statistical variance of the coefficient distribution in macro-blocks tends to decrease after data hiding. These phenomena are analyzed mathematically. Then, statistical features in the pixel, DCT, and DWT domains are extracted and a maximum likelihood ratio test is adopted to solve the hiding domain classification problem. The proposed scheme has demonstrated good classification results.