基于带全局重同步机制的句子嵌入空间的鲁棒无载体语言隐写术
Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization
- Nanjing University of Information Science and Technology（南京信息工程大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有语言隐写术在文本扰动下可靠性差及比特滑移问题，提出基于句子嵌入空间的无载体隐写框架，引入GRM机制解耦局部嵌入与全局消息恢复，提升了鲁棒性等性能。
AI中文摘要：
语言隐写术可通过自然语言实现隐秘通信。现有方法严重依赖词元级操作，在词级和句子级文本扰动下难以保持可靠性；此外，基于变长编码的方案在微小扰动下极易发生比特滑移，因为扰动会导致嵌入与提取的比特序列之间失同步。为解决这些问题，我们提出一种鲁棒无载体隐写框架，该框架在句子嵌入空间而非词元空间运行。具体而言，秘密消息被编码为句子嵌入空间中的层次聚类路径，这增强了针对词级和句子级文本扰动的解码稳定性。为解决比特滑移问题，我们引入全局重同步机制（Global Resynchronization Mechanism, GRM），该机制将变长比特流重构为锚定在语义子空间的离散符号，使局部嵌入故障与全局消息恢复解耦。实验结果表明，在词级和句子级扰动下，我们的方法在鲁棒性方面取得了显著提升，同时保持了有效的嵌入容量，并表现出对统计分析的强抗性。
英文摘要：
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.
