  With the rapid development of natural language processing technologies, more
and more text steganographic methods based on automatic text generation
technology have appeared in recent years. These models use the powerful
self-learning and feature extraction ability of the neural networks to learn
the feature expression of massive normal texts. Then they can automatically
generate dense steganographic texts which conform to such statistical
distribution based on the learned statistical patterns. In this paper, we
observe that the conditional probability distribution of each word in the
automatically generated steganographic texts will be distorted after embedded
with hidden information. We use Recurrent Neural Networks (RNNs) to extract
these feature distribution differences and then classify those features into
cover text and stego text categories. Experimental results show that the
proposed model can achieve high detection accuracy. Besides, the proposed model
can even make use of the subtle differences of the feature distribution of
texts to estimate the amount of hidden information embedded in the generated
steganographic text.
