Coverless Text Hiding Method Based on Improved Evaluation Index and One-Bit Embedding

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.