Abstract
Recently, deep learning has been used in steganalysis based on convolutional neural networks (CNN). In this work, we propose a CNN architecture (the so-called maxCNN) to use the selection channel. It is the first time that the knowledge of the selection channel has been incorporated into CNN for steganalysis. The proposed method assigns large weights to features learned from complex texture regions while assigns small weights to features learned from smooth regions. Experimental results on the well-known dataset BOSSbase have demonstrated that the proposed scheme is able to improve detection performance, especially for low embedding payloads. The results have shown that with the ensemble of maxCNN and maxSRMd2+EC, the proposed method can obtain better performance compared with the reported state-of-the-art on detecting WOW embedding algorithm.
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Acknowledgements
This work was supported by NSFC (Grant nos. U1536204, 61379155), Special funding for basic scientific research of Sun Yat-sen University (6177060230).
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Yang, J., Liu, K., Kang, X., Wong, E., Shi, Y. (2017). Steganalysis Based on Awareness of Selection-Channel and Deep Learning. In: Kraetzer, C., Shi, YQ., Dittmann, J., Kim, H. (eds) Digital Forensics and Watermarking. IWDW 2017. Lecture Notes in Computer Science(), vol 10431. Springer, Cham. https://doi.org/10.1007/978-3-319-64185-0_20
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DOI: https://doi.org/10.1007/978-3-319-64185-0_20
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