STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model

Recent provably secure linguistic steganography (PSLS) methods rely on mainstream autoregressive language models (ARMs) to address historically challenging tasks, that is, to disguise covert communication as ``innocuous'' natural language communication. However, due to the characteristic of sequential generation of ARMs, the stegotext generated by ARM-based PSLS methods will produce serious error propagation once it changes, making existing methods unavailable under an active tampering attack. To address this, we propose a robust, provably secure linguistic steganography with diffusion language models (DLMs). Unlike ARMs, DLMs can generate text in a partially parallel manner, allowing us to find robust positions for steganographic embedding that can be combined with error-correcting codes. Furthermore, we introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction. Theoretical proof and experimental results demonstrate that our method is secure and robust. It can resist token ambiguity in stegotext segmentation and, to some extent, withstand token-level attacks of insertion, deletion, and substitution.

Authors

• Yuang Qi Anhui Province Key Laboratory of Digital Security University of Science and Technology of China
• Na Zhao Singapore University of Technology and Design
• Qiyi Yao
• Benlong Wu
• Weiming Zhang University of Science and Technology of China
• Nenghai Yu School of Cyber Science and Technology, University of Science and Technology of China Anhui Province Key Laboratory of Digital Security the CCCD Key Lab of Ministry of Culture and Tourism
• Kejiang Chen University of Science and Technology of China

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