Problem
Research questions and friction points this paper is trying to address.
Linguistic Steganography
Textual Perturbations
Bit-Slippage
Sentence Embedding Space
Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization
🤖 AI Summary
本文提出一种基于句子嵌入空间的无载体语言隐写框架，通过层次聚类路径编码秘密信息，并引入全局重同步机制解决位滑移问题，提高抗文本扰动能力。
📝 Abstract
Linguistic steganography enables covert communication through natural language. Existing methods heavily rely on token-level operations and struggle to maintain reliability under word- and sentence-level textual perturbations. Moreover, variable-length coding-based schemes are highly susceptible to bit-slippage under minor disturbances, as perturbations cause desynchronization between embedded and extracted bit sequences. To address these issues, we propose a robust coverless steganographic framework that operates in the sentence embedding space rather than the token space. Specifically, secret messages are encoded as hierarchical clustering paths in the sentence embedding space, which enhances decoding stability against word- and sentence-level textual perturbations. To tackle the bit-slippage problem, we introduce a Global Resynchronization Mechanism (GRM) that reframes variable-length bitstreams as discrete symbols anchored to semantic subspaces, decoupling local embedding failures from global message recovery. Experimental results demonstrate that under word- and sentence-level perturbations, our approach achieves substantial improvements in robustness, while maintaining effective embedding capacity and exhibiting strong resistance to statistical analysis.
Innovation
Methods, ideas, or system contributions that make the work stand out.
sentence embedding space
hierarchical clustering paths
Global Resynchronization Mechanism (GRM)
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