Content-Adaptive Residual for Steganalysis

This paper employs the concept of the content-adaptive residual and presents a low-dimensional feature set for detecting the grayscale steganography in spatial domain. The testing image is first segmented into three kinds of areas, that is, the smooth, edge, and textural areas. Then, different pixel predictors are used to calculate the residuals responded to different areas. The yielded different co-occurrence matrices are finally collected as the steganalytic features. Experiments reported show that the proposed method is effective and yields good performances when detecting popular steganographic algorithms such as LSB matching, EA, and HUGO.

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