Open Access

ARTICLE

Topic Controlled Steganography via Graph-to-Text Generation

Bowen Sun¹, Yamin Li^1,2,3,*, Jun Zhang¹, Honghong Xu¹, Xiaoqiang Ma⁴, Ping Xia^2,3,5

1 School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, China
2 Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China
3 Yichang Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China
4 Department of CSIS, Douglas College, New Westminster, BC, V3L 5B2, Canada
5 College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

* Corresponding Author: Yamin Li. Email:

Computer Modeling in Engineering & Sciences 2023, 136(1), 157-176. https://doi.org/10.32604/cmes.2023.025082

Received 21 June 2022; Accepted 06 September 2022; Issue published 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 in concealment. In this paper, we propose a topic-controlled steganography method which is guided by graph-to-text generation. The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs, and the topic of the generated texts is controllable. We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation. Different from traditional linguistic steganography methods, we encode the secret information during graph path coding rather than using conditional probability. We test our method in different aspects and compare it with other text generative steganographic methods. The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

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Keywords

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