Text Steganography with High Embedding Rate: Using Recurrent Neural Networks to Generate Chinese Classic Poetry

We propose a novel text steganography method using RNN Encoder-Decoder structure to generate quatrains, one genre of Chinese poetry. Compared to other text-generation based steganography methods which have either very low embedding rate or flaws in the naturalness of generated texts, our method has higher embedding rate and better text quality. In this paper, we use the LSTM Encoder-Decoder model to generate the first line of a quatrain with a keyword and then generate the following lines one by one. RNN has proved effective in generating poetry, but when applied to steganograpy, poetry quality decreases sharply, because of the redundancy we create to hide information. To overcome this problem, we propose a template-constrained generation method and develop a word-choosing approach using inner-word mutual information. Through a series of experiments, it is proven that our approach outperforms other poetry steganography methods in both embedding rate and poetry quality.