Abstract
Steganography based on texts has always been a hot but extremely hard research topic. Due to the high coding characteristics of the text compared to other information carriers, the redundancy of information is very low, which makes it really difficult to hide information inside. In this paper, combined with the recurrent neural network (RNN) and reinforcement learning (RL), we designed and implemented a real-time interactive text steganography model (RITS). The proposed model can automatically generate semantically coherent and syntactically correct dialogues based on the input sentence, through the reasonable encoding of the text in the dialog generation process to realize secret information hiding and transmission. We trained our model using publicly collected datasets which contains 5808 dialogues and evaluated the proposed model from several perspectives. Experimental results show that the proposed model can be very efficient to implement the embedding and extraction of information. The generated dialogue texts are of high quality which shows high concealment.
This work is supported by the National Key Research and Development Program of China (No. 2016YFB0800402) and the National Natural Science Foundation of China (No. U1405254, U1536201, U1705261).
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Yang, Z., Zhang, P., Jiang, M., Huang, Y., Zhang, YJ. (2018). RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_24
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