RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks

• Published in: IEEE transactions on information forensics and security Vol. 14; no. 5; pp. 1280 - 1295
• Main Authors: Yang, Zhong-Liang, Guo, Xiao-Qing, Chen, Zi-Ming, Huang, Yong-Feng, Zhang, Yu-Jin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: automatic text generation; Coding; Conditional probability; Encoding; Linguistic steganography; Linguistics; Model testing; Neural networks; Probability distribution; recurrent neural network; Recurrent neural networks; Semantics; State of the art; Statistical analysis; Statistical methods; Steganography; Training; Watermarking
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2018.2871746

Linguistic steganography based on text carrier auto-generation technology is a current topic with great promise and challenges. Limited by the text automatic generation technology or the corresponding text coding methods, the quality of the steganographic text generated by previous methods is inferior, which makes its imperceptibility unsatisfactory. In this paper, we propose a linguistic steganography based on recurrent neural networks, which can automatically generate high-quality text covers on the basis of a secret bitstream that needs to be hidden. We trained our model with a large number of artificially generated samples and obtained a good estimate of the statistical language model. In the text generation process, we propose fixed-length coding and variable-length coding to encode words based on their conditional probability distribution. We designed several experiments to test the proposed model from the perspectives of information hiding efficiency, information imperceptibility, and information hidden capacity. The experimental results show that the proposed model outperforms all the previous related methods and achieves the state-of-the-art performance.