Linguistic Steganography Based on Sentence Attribute Encoding

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 from the secret information to be embedded, a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute, thereby improving the problem of insufficient manual rules. During the extraction, secret information can be extracted solely by employing the semantic attribute analyzer, thereby eliminating the dependence on the paraphrasing generation model. Experimental results show that this method achieves an extraction speed of 1141.54 bits/sec, compared with the existing methods, it has remarkable advantages regarding extraction speed. Meanwhile, the stego text generated by this method respectively reaches 68.53, 39.88, and 80.77 on BLEU, PPL, and BERTScore. Compared with the existing methods, the text quality is effectively improved.