Work

Research on Making Two Models Based on the Generative Linguistic Steganography for Securing Linguistic Steganographic Texts from Active Attacks

Year: 2025

Type: article

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 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.

Source: Symmetry

DOI: 10.3390/sym17091416

Authors: Yingquan Chen, Qianmu Li, Xiaocong Wu, Zijian Ying

Institutions: Nanjing University of Science and Technology, Tarim University

Language: English

Topic: Advanced Steganography and Watermarking Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Government of Jiangsu Province