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Different from the conventional deep learning work based on an images contentin computer vision, deep steganalysis is an art to detect the secretinformation embedded in an image via deep learning, pose challenge of detectionweak information invisible hidden in a host image thus learning in a very lowsignal-to-noise (SNR) case. In this paper, we propose a 32- layer convolutionalneural Networks (CNNs) in to improve the efficiency of preprocess and reuse thefeatures by concatenating all features from the previous layers with the samefeature- map size, thus improve the flow of information and gradient. Theshared features and bottleneck layers further improve the feature propagationand reduce the CNN model parameters dramatically. Experimental results on theBOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNNarchitecture can improve the performance and enhance the robustness. To furtherboost the detection accuracy, an ensemble architecture called as CNN-SCA-GFR isproposed, CNN-SCA- GFR is also the first work to combine the CNN architectureand conventional method in the JPEG domain. Experiments show that it canfurther lower detection errors. Compared with the state-of-the-art method XuNet[1] on BOSSbase, the proposed CNN-SCA-GFR architecture can reduce detectionerror rate by 5.67% for 0.1 bpnzAC and by 4.41% for 0.4 bpnzAC while the numberof training parameters in CNN is only 17% of what used by XuNet. It alsodecreases the detection errors from the conventional method SCA-GFR by 7.89%for 0.1 bpnzAC and 8.06% for 0.4 bpnzAC, respectively.

JPEG Steganalysis Based on DenseNet