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  • Open Access

    ARTICLE

    Linguistic Steganography Based on Sentence Attribute Encoding

    Lingyun Xiang*, Xu He, Xi Zhang, Chengfu Ou

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025

    Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >

  • Open Access

    ARTICLE

    Topic Controlled Steganography via Graph-to-Text Generation

    Bowen Sun1, Yamin Li1,2,3,*, Jun Zhang1, Honghong Xu1, Xiaoqiang Ma4, Ping Xia2,3,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 157-176, 2023, DOI:10.32604/cmes.2023.025082 - 05 January 2023

    Abstract Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement… More >

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