Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding
This addresses the problem of secure data transmission for users needing privacy in cyberspace, but it is incremental as it builds on existing steganography techniques.
The paper tackles the challenge of hiding information in text, which is difficult due to high coding and low redundancy, by proposing a method that automatically generates steganographic text using a Markov chain model and Huffman coding, resulting in superior performance in imperceptibility and hidden capacity compared to previous methods.
Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. The text is the most widely used information carrier in people's daily life, using text as a carrier for information hiding has broad research prospects. However, due to the high coding degree and less information redundancy in the text, it has been an extremely challenging problem to hide information in it for a long time. In this paper, we propose a steganography method which can automatically generate steganographic text based on the Markov chain model and Huffman coding. It can automatically generate fluent text carrier in terms of secret information which need to be embedded. The proposed model can learn from a large number of samples written by people and obtain a good estimate of the statistical language model. We evaluated the proposed model from several perspectives. Experimental results show that the performance of the proposed model is superior to all the previous related methods in terms of information imperceptibility and information hidden capacity.