  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.
