Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography
This work addresses the problem of robust and imperceptible steganography for secure communication, representing an incremental improvement over prior methods that limited context windows.
The paper tackles the fragility of linguistic steganography to minor text modifications by proposing the anchored sliding window (ASW) framework, which improves imperceptibility and robustness while maintaining text quality, with experiments showing significant and consistent outperformance over baseline methods.
Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. While previous work mitigates this fragility by limiting the context window, it significantly compromises text quality. In this paper, we propose the anchored sliding window (ASW) framework to improve imperceptibility and robustness. In addition to the latest tokens, the prompt and a bridge context are anchored within the context window, encouraging the model to compensate for the excluded tokens. We formulate the optimization of the bridge context as a variant of prompt distillation, which we further extend using self-distillation strategies. Experiments show that our ASW significantly and consistently outperforms the baseline method in text quality, imperceptibility, and robustness across diverse settings. The code is available at github.com/ryehr/ASW_steganography.