Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding

TL;DR

This paper introduces an automatic steganography method that generates fluent, imperceptible text carrying hidden information using a Markov model and Huffman coding, outperforming previous methods in capacity and imperceptibility.

Contribution

It presents a novel automatic text steganography approach combining Markov chains and Huffman coding, enhancing hidden capacity and imperceptibility.

Findings

Outperforms previous methods in information imperceptibility.

Achieves higher hidden capacity in generated texts.

Produces fluent, natural-looking steganographic text.

Abstract

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.