Robustness to Word- and Sentence-Level Rewriting in Text Steganography

Develop robustness mechanisms for current text steganography frameworks that reliably preserve message recovery under practical word- and sentence-level rewriting attacks, including deletion, insertion, and paraphrasing, when adversaries are unaware of the steganographic system’s segmentation rules.

Background

Existing robust linguistic steganography methods primarily defend against token-level perturbations. However, real-world adversaries may modify text at the word or sentence level without knowing the system’s segmentation rules, for example by deleting or inserting words or paraphrasing sentences. Such operations can disrupt token-level defenses and cause decoding failures. The paper presents its sentence-embedding-space framework as an approach to this broader robustness issue, but characterizes the challenge as unresolved for current text steganography frameworks.

This problem concerns establishing reliable message extraction despite semantic-preserving rewriting that may alter word boundaries, sentence structure, or surface realizations. Solving it would improve the practical applicability of linguistic steganography in communication environments where transmitted text can be actively edited or paraphrased.