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Conventional state-of-the-art image steganalysis approaches usually consistof a classifier trained with features provided by rich image models. As bothfeatures extraction and classification steps are perfectly embodied in the deeplearning architecture called Convolutional Neural Network (CNN), differentstudies have tried to design a CNN-based steganalyzer. The network designed byXu et al. is the first competitive CNN with the combination Spatial Rich Models(SRM) and Ensemble Classifier (EC) providing detection performances of the sameorder. In this work we propose a criterion to choose either the CNN or theSRM+EC method for a given input image. Our approach is studied with threedifferent steganographic spatial domain algorithms: S-UNIWARD, MiPOD, and HILL,using the Tensorflow computing platform, and exhibits detection capabilitiesbetter than each method alone. Furthermore, as SRM+EC and the CNN are both onlytrained with a single embedding algorithm, namely MiPOD, the proposed methodcan be seen as an approach for blind steganalysis. In blind detection, errorrates are respectively of 16% for S-UNIWARD, 16% for MiPOD, and 17% for HILL onthe BOSSBase with a payload of 0.4 bpp. For 0.1 bpp, the respectivecorresponding error rates are of 39%, 38%, and 41%, and are always better thanthe ones provided by SRM+EC.

Improving Blind Steganalysis in Spatial Domain using a Criterion to Choose the Appropriate Steganalyzer between CNN and SRM+EC