Content-adaptive steganalysis for color images

Some steganography methods for gray-scale image can be extended to true RGB color image by treating each of its three color channels as a gray-scale image. In modern popular steganography, most embedding changes are highly concentrated on those complex textural regions with smaller embedding distortions. However, the existing steganalysis methods for color images directly extract steganalytic features from the whole image. In this paper, we propose a content-adaptive steganalysis strategy for color images. The new strategy aims to extract spatial rich model features from each color channel and just extract color rich model features from the pixels that may have been modified. In order to locate those suspected pixels, we first calculate the embedding costs of each channel, and then a subset of pixels with smaller embedding costs is selected. Experimental results show that the proposed strategy performs better than the state-of-the-art color image steganalysis method. Copyright (C) 2017 John Wiley & Sons, Ltd.