  Steganalysis has been an important research topic in cybersecurity that helps
to identify covert attacks in public network. With the rapid development of
natural language processing technology in the past two years, coverless
steganography has been greatly developed. Previous text steganalysis methods
have shown unsatisfactory results on this new steganography technique and
remain an unsolved challenge. Different from all previous text steganalysis
methods, in this paper, we propose a text steganalysis method(TS-CNN) based on
semantic analysis, which uses convolutional neural network(CNN) to extract
high-level semantic features of texts, and finds the subtle distribution
differences in the semantic space before and after embedding the secret
information. To train and test the proposed model, we collected and released a
large text steganalysis(CT-Steg) dataset, which contains a total number of
216,000 texts with various lengths and various embedding rates. Experimental
results show that the proposed model can achieve nearly 100\% precision and
recall, outperforms all the previous methods. Furthermore, the proposed model
can even estimate the capacity of the hidden information inside. These results
strongly support that using the subtle changes in the semantic space before and
after embedding the secret information to conduct text steganalysis is feasible
and effective.
