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Keywords = continuous ODRTA (CODRTA)

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26 pages, 2814 KB  
Article
Research on Making Two Models Based on the Generative Linguistic Steganography for Securing Linguistic Steganographic Texts from Active Attacks
by Yingquan Chen, Qianmu Li, Xiaocong Wu and Zijian Ying
Symmetry 2025, 17(9), 1416; https://doi.org/10.3390/sym17091416 - 1 Sep 2025
Viewed by 1249
Abstract
Generative steganographic text covertly transmits hidden information through readable text that is unrelated to the message. Existing AI-based linguistic steganography primarily focuses on improving text quality to evade detection and therefore only addresses passive attacks. Active attacks, such as text tampering, can disrupt [...] Read more.
Generative steganographic text covertly transmits hidden information through readable text that is unrelated to the message. Existing AI-based linguistic steganography primarily focuses on improving text quality to evade detection and therefore only addresses passive attacks. Active attacks, such as text tampering, can disrupt the symmetry between encoding and decoding, which in turn prevents accurate extraction of hidden information. To investigate these threats, we construct two attack models: the in-domain synonym substitution attack (ISSA) and the out-of-domain random tampering attack (ODRTA), with ODRTA further divided into continuous (CODRTA) and discontinuous (DODRTA) types. To enhance robustness, we propose a proactive adaptive-clustering defense against ISSA, and, for CODRTA and DODRTA, a post-hoc repair mechanism based on context-oriented search and the determinism of text generation. Experimental results demonstrate that these mechanisms effectively counter all attack types and significantly improve the integrity and usability of hidden information. The main limitation of our approach is the relatively high computational cost of defending against ISSA. Future work will focus on improving efficiency and expanding practical applicability. Full article
(This article belongs to the Section Computer)
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