Robust steganography without embedding based on secure container synthesis and iterative message recovery
Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak message recovery robustness, synthesis fidelity, and the risk of message leakage. To address these problems, we propose a novel robust steganography without embedding method in this paper. In particular, we design a secure weight modulation-based generator by introducing secure factors to hide secret messages in synthesised container images. In this manner, the synthesised results are modulated by secure factors and thus the secret messages are inaccessible when using fake factors, thus reducing the risk of message leakage. Furthermore, we design a difference predictor via the reconstruction of tampered container images together with an adversarial training strategy to iteratively update the estimation of hidden messages. This ensures robustness of recovering hidden messages, while degradation of synthesis fidelity is reduced since the generator is not included in the adversarial training. Extensive experimental results convincingly demonstrate that our proposed method is effective in avoiding message leakage and superior to other existing methods in terms of recovery robustness and synthesis fidelity.
National Innovation 2030 Major S&T Project of China (2020AAA0104203)
National Natural Science Foundation of China (62006007, 61602527)
Natural Science Foundation of Hunan Province, China (2022GK5002, 2020JJ4746)
Special Foundation for Distinguished Young Scientists of Changsha (kq2209003)
111 Project (No. D23006)
- Computer Science
Published inProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Source32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)
PublisherInternational Joint Conferences on Artificial Intelligence (IJCAI)
- AM (Accepted Manuscript)
Rights holder© International Joint Conferences on Artificial Intelligence (IJCAI)
Publisher statementCopyright © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.