Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns (LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.