關鍵字：隱寫分析、隱寫術、深度學習、卷積神經網路。;Steganography is a technique to embed a large amount of information in such carriers as images, audio, videos and even texts to achieve effective secret communications. On the other hand, steganalysis is the adversarial technique aimed at determining whether the investigated carriers contain hidden information. In the field of steganalysis, heuristic features were usually adopted. Recently deep learning techniques are often employed but most existing methods still use certain high-pass filters to apply pre-processing. In this research, we focus on image steganalysis and adopt the dual path networks (DPN) to achieve an end-to-end architecture. The proposed scheme uses ResNet to extract features, and then employs DenseNet to extract deeper and smaller features. It combines the advantages of both networks to form a DPN blocks with shared weights. The scheme uses the group convolution to reduce the amount of computation. Finally, dual path blocks with different parameters are tested to build suitable steganalysis architectures. SRNet, which uses ResNet, performs quite well in image steganalysis. We first replace its ResNet blocks with DPN blocks for comparison. The detection accuracy is improved and confirms that the structure using DPN is helpful to steganalysis. We then use DPN blocks to build our architecture and then compare the performance with the existing steganalysis architectures. Finally, we use the ALASKA II dataset to verify the feasibility of the proposed scheme. Index Terms - Steganalysis, steganography, deep learning, convolutional neural networks.
