Learning Symmetric and Asymmetric Steganography via Adversarial Training
This work addresses the challenge of secure and undetectable steganography for applications in data security and privacy, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of concealing secret messages in media carriers by proposing a novel key-dependent steganographic scheme using adversarial training, achieving a 20% improvement in invisibility over previous deep-learning methods and 25% better undetectability than classic algorithms.
Steganography refers to the art of concealing secret messages within multiple media carriers so that an eavesdropper is unable to detect the presence and content of the hidden messages. In this paper, we firstly propose a novel key-dependent steganographic scheme that achieves steganographic objectives with adversarial training. Symmetric (secret-key) and Asymmetric (public-key) steganographic scheme are separately proposed and each scheme is successfully designed and implemented. We show that these encodings produced by our scheme improve the invisibility by 20% than previous deep-leanring-based work, and further that perform competitively remarkable undetectability 25% better than classic steganographic algorithms. Finally, we simulated our scheme in a real situation where the decoder achieved an accuracy of more than 98% of the original message.