Abstract
With the development of Generative Adversarial Networks (GAN), GAN-based steganography and steganalysis techniques have attracted much attention from researchers. In this paper, we propose a novel image steganography method without modification based on Wasserstein GAN Gradient Penalty (WGAN-GP). The proposed architecture has a generative network, a discriminative network, and an extractor network. The Generator is used to generate the cover image (also is the stego image), and the Extractor is used to extract secret information. During the process of stego image generation, no modification operations are required. To make full use of the learning ability of convolutional neural networks and GAN, we synchronized the training of Generator and Extractor. Experiment results show that the proposed method has the advantages of higher recovery accuracy and higher training efficiency.
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Acknowledgment
This paper was supported by the National Natural Science Foundation of China (Grant No. 61872384), and Basic research foundation for Engineering University of PAP(Grant No. WJY201918).
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Li, J. et al. (2020). A Generative Steganography Method Based on WGAN-GP. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_34
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DOI: https://doi.org/10.1007/978-981-15-8083-3_34
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