Content Adaptive Image Steganalysis in Spatial Domain Using Selected Co-Occurrence Features

In this paper, a general content adaptive image steganography detector in the spatial domain is proposed. We assemble conventional Haar and LBP features to construct local co-occurrence features, then the boosted classifiers are used to assemble the features as well as the final detector, and each weak classifier of the boosted classifiers corresponds to the co-occurrence feature of a local image region. Moreover, the classification ability and the generalization power of the candidate features are both evaluated for decision in the feature selection procedure of boosting training, which makes the final detector more accuracy. The experimental results on standard dataset show that the proposed framework can detect two primary content adaptive stego algorithms in the spatial domain with higher accuracy than the state-of-the-art steganalysis method.