TS-CSW: text steganalysis and hidden capacity estimation based on convolutional sliding windows

With the rapid development of natural language processing (NLP) technology in the past few years, the automatic steganographic texts generation methods have been greatly developed. Benefiting from the powerful feature extraction and expression capabilities of neural networks, these methods can generate steganographic texts with both relatively high concealment and high hidden capacity at the same time. For these steganographic methods, previous steganalysis models show unsatisfactory detection performance, which remains an unsolved problem and poses a great threat to the security of cyberspace. In this paper, we first collect a large text steganalysis (T-Steg) dataset, which contains a total number of 396,000 texts with various embedding rates under various formats. We analyze that there are three kinds of word correlation patterns in texts. Then we propose a new text steganalysis model based on convolutional sliding windows (TS-CSW), which use convolutional sliding windows (CSW) with multiple sizes to extract those correlation features. We observed that these word correlation features in the generated steganographic texts would be distorted after being embedded with secret information. These subtle changes of correlation feature distribution could then be used for text steganalysis. We use the samples collected in T-Steg dataset to train and test the proposed steganalysis method. Experimental results show that the proposed model can not only achieve a high steganalysis performance, but can even estimate the amount of secret information embedded in the generated steganographic texts, which shows a state-of-the-art performance.