Region based Image Steganalysis using Artificial Bee Colony

Steganalysis is the art and skill of discriminating stego images from cover images. Image steganalysis algorithms can be divided into two broad categories, specific and universal. In this paper, a novel universal image steganalysis algorithm is proposed which is called RISAB, Region based Image Steganalysis using Artificial Bee colony. The goal of the proposed method is to realize a sub-image from stego and cover images through ABC with respect to density according to the cover, stego and difference images. In our method, we look for the best sub-image, which contains the highest density with respect to the changed embedding pixels. Furthermore, after selecting the best sub-image, we extract the features, which have been selected by IFAB, Image steganalysis based on Feature selection using Artificial Bee colony. At the end, both selected features by IFAB and extracted features by RISAB are combined. As a result, a feature vector is generated which improves accuracy of steganalysis. Experimental results show that our proposed method outperforms other approaches.