Abstract
Existing steganographic methods based on adversarial images can only design adversarial images for a single steganalyzer and cannot resist detection from the latest steganalyzers using convolutional neural networks such as SRNet and Zhu-Net. To address this issue, this paper proposes a novel method for enhancing the security of image steganography using multiple adversarial networks and channel attention modules. The proposed method employs generative adversarial networks based on the U-Net structure to generate high-quality adversarial images and uses the self-learning properties of the adversarial networks to iteratively optimize the parameters of multiple adversarial steganographic networks. This process generates high-quality adversarial images capable of misleading multiple steganalyzers. Additionally, the proposed scheme adaptsively adjusts the distribution of adversarial noise in the original image using multiple lightweight channel attention modules in the generator, thus enhancing the anti-steganalysis ability of adversarial images. Furthermore, the proposed method utilizes multiple discrimination losses and MSE loss, dynamically combined to improve the quality of adversarial images and facilitate the network’s rapid and stable convergence. Extensive experimental results demonstrate that the proposed algorithm can generate adversarial images with a PSNR of up to 42.8 dB, and the success rate of misleading the advanced steganalyzers is over 93%. The security and generalization of the algorithm we propose exceed those of the compared steganographic methods.
This work was partially supported by National Natural Science Foundation of China (62272255); National key research and development program of China (2021YFC3340602); Shandong Provincial Natural Science Foundation Innovation and Development Joint Fund (ZR202208310038); Ability Improvement Project of Science and Technology SMES in Shandong Province (2022TSGC2485); Project of Jinan Research Leader Studio (2020GXRC056); Project of Jinan Introducing Innovation Team (202228016); Project of Jinan City-School Integration Development (JNSX2021030); Youth Innovation Team of Colleges and Universities in Shandong Province (2022KJ124)；The “Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education (HZKY20220482)；Shandong Provincial Natural Science Foundation (ZR2020MF054).
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Ma, B., Li, K., Xu, J., Wang, C., Li, J., Zhang, L. (2023). Enhancing the Anti-steganalysis Ability of Image Steganography via Multiple Adversarial Networks. In: Yung, M., Chen, C., Meng, W. (eds) Science of Cyber Security . SciSec 2023. Lecture Notes in Computer Science, vol 14299. Springer, Cham. https://doi.org/10.1007/978-3-031-45933-7_11
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