Abstract
The research of steganography has been one of the hotspots in the field of information security. The automatic distortion learning steganographic method based on generative adversarial networks (GAN) has outperformed the hand-crafted steganographic algorithms. However, the detection accuracy of steganalysis networks keeps improving as they are continuously combined with the latest neural network structures. It poses a great threat to the security of steganographic algorithms. Therefore, how to enhance the imperceptibility of secret messages is an ongoing issue. In this paper, we propose an automatic distortion learning steganographic method with better security performance based on image edge enhancement. To reduce the search space and increase the training efficiency, we change the input of the network by feeding edge-enhanced images to generator. In addition, to make the pixel change probability map more accurate and hereby improve the abaility to resist steganalyzer, we add shallower connections to the U-Net structure to build the generator. After this, the generated pixel change probability map is further used to simulate information embedding. Under this framework, we use XuNet to judge the imperceptibility of secret messsages in time and adjust the pixel change probability. The experimental results on BOSSBase dataset show that our proposed method performs better than the hand-crafted (S-UNIWARD) and GAN-based automatic steganographic algorithms (UT-GAN) on security against XuNet, SRNet and SRM.
Supported by the National Natural Science Foundation of China under grant U1836110, U1836208; by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20200039.
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Li, E., Fu, Z., Chen, J. (2021). Adaptive Steganography Based on Image Edge Enhancement and Automatic Distortion Learning. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_13
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