Coverless Text Hiding Method Based on Improved Evaluation Index and One-Bit Embedding
- Ning Wu1,2, Yi Yang1,*, Lian Li1, Zhongliang Yang3, Poli Shang4, Weibo Ma5, Zhenru Liu5
1 School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
2 School of Computer Science & Artificial Intelligence Lanzhou Institute of Technology, Lanzhou, 730050, China
3 Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
4 School of Electronic and Electrical Engineering, Lanzhou Petrochemical Polytechnic, Lanzhou, 730060, China
5 School of Electrical Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
* Corresponding Author: Yi Yang. Email:
(This article belongs to this Special Issue: Information Hiding and Multimedia Security)
2 School of Computer Science & Artificial Intelligence Lanzhou Institute of Technology, Lanzhou, 730050, China
3 Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
4 School of Electronic and Electrical Engineering, Lanzhou Petrochemical Polytechnic, Lanzhou, 730060, China
5 School of Electrical Engineering, Lanzhou Institute of Technology, Lanzhou, 730050, China
* Corresponding Author: Yi Yang. Email:
(This article belongs to this Special Issue: Information Hiding and Multimedia Security)
Received 05 March 2020; Accepted 04 June 2020; Issue published 21 August 2020
Abstract
In the field of information hiding, text is less redundant, which leads to
less space to hide information and challenging work for researchers. Based on the
Markov chain model, this paper proposes an improved evaluation index and onebit embedding coverless text steganography method. In the steganography process, this method did not simply take the transition probability as the optimization
basis of the steganography model, but combined it with the sentence length in the
corresponding nodes in the model to gauge sentence quality. Based on this, only
two optimal conjunctions of the current words are retained in the method to generate sentences of higher quality. Because the size of the training text dataset is
generally large, this leads to higher complexity of the steganographic model;
hence, fewer repetitions of the generated steganographic sentences occur. Different datasets and methods were selected to test the quality of the model. The results
indicate that our method can achieve higher hiding capacity and has better concealment capability.
Keywords
Coverless text steganography; Markov chain; evaluation index; onebit embedding
Cite This Article
Wu, N., Yang, Y., Li, L., Yang, Z., Shang, P. et al. (2020). Coverless Text Hiding Method Based on Improved Evaluation Index and One-Bit Embedding. CMES-Computer Modeling in Engineering & Sciences, 124(3), 1035–1048.
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