Abstract
Image has been widely studied as an effective carrier of information steganography, however, low steganographic capacity is a technical problem that has not been solved in non-embedded steganography methods. In this paper, we proposed a carrier-free steganography method based on Wasserstein GAN. We segmented the target information and input it into the trained Wasserstein GAN, and then generated the visual-real image. The core design is that the output results are converted into images in the trained network according to the mapping relationship between preset coding information and random noise. The experimental results indicated that the proposed method can effectively improve the ability of steganography. In addition, the results also testified that the proposed method does not depend on the complex neural network structure. On this basis, we further proved that by changing the length of noise and the mapping relationships between coding information and noise, the number of generated images can be reduced, and the steganography ability and efficiency of the algorithm can be improved.
This work was supported in part by the National Natural Science Foundation of China under Grant U2003206 and 62106060; and in part by the Natural Science Base Research Plan in Shaanxi Province of China under Grant 2018JM6103.
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Yu, X., Cui, J., Liu, M. (2022). An Embedding Carrier-Free Steganography Method Based on Wasserstein GAN. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_35
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