Linguistic Steganography: Hiding Information in Syntax Space
Lingyun Xiang, Chengfu Ou, Daojian Zeng · IEEE Signal Processing Letters · 2023
To enhance the embedding capacity, the existing linguistic steganography methods predominantly focus on the word or phrase level, with limited emphasis on the sentence level. Nevertheless, these approaches exhibit a deficiency in achieving an optimal balance between embedding capacity and semantic coherence. Moreover, compromised semantic coherence can potentially increase security risks. In this paper, we propose a novel sentence-levelSteganography framework toHideInformation inSyntaxSpace (HISS-Stega) that enables larger embedding capacity while preserving better semantic coherence. Specifically, HISS-Stega builds a syntax-controlled paraphrase generation model to automatically modify the expression forms of the covertext, thereby augmenting the diversity of transformations. This enhancement contributes to the overall improvement in embedding capacity. Subsequently, a syntactic bins coding strategy is employed for successfully embedding secret information in the generated syntax space. Furthermore, HISS-Stega incorporates a semantic distortion function aimed at identifying the optimal syntactic structure for concealing secret information, thereby ensuring enhanced semantic coherence and mitigating potential security risks. The experimental results demonstrate that, in comparison to existing methods, HISS-Stega not only enhances the embedding capacity of each sentence but also maintains a high level of semantic coherence and anti-steganalysis capability.