SECC-Stega: Generative Linguistic Steganographic Framework Based on Error Correcting Codes

Yuzhe Guo, Zhongliang Yang, Zhuang Wang, Zhili Zhou, Linna Zhou

Abstract: With the rise and maturation of neural network technology, generative text steganography based on language models is gradually becoming the mainstream technique in text steganography. However, homomorphic extraction attacks and text modification attacks from third parties pose serious threats to the usability of generative text steganography. To address this issue, this paper proposes a generative text steganography algorithm framework based on error correction codes. This framework enhances the robustness and security of steganography by encoding the secret information. Experimental results verify that the proposed framework achieves the expected outcomes and exhibits a certain degree of generality.

External IDs:dblp:conf/icassp/GuoYWZZ25