Deep learning based language models have improved generation-based linguistic steganography, posing a huge challenge for linguistic steganalysis. The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features. To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-shortterm-memory (BiLSTM) and short-cut dense convolutional neural network (CNN). The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence. The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix. We connect two structures in parallel, concatenate the long dependency representations and the local semantic features, and classify the stego and cover texts. The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.

Traditional text steganalysis methods rely on a large amount of labeled data. At the same time, the test data should be independent and identically distributed with the training data. However, in practice, a large number of text types make it difficult to satisfy the i.i.d condition between the training set and the test set, which leads to the problem of domain mismatch and significantly reduces the detection performance. In this paper, we draw on the ideas of domain adaptation and transductive learning to design a novel text steganalysis method. In this method, we design a distributed adaptation layer and adopt three loss functions to achieve domain adaptation, so that the model can learn the domaininvariant text features. The experimental results show that the method has better steganalysis performance in the case of domain mismatch. CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • Computing methodologies → Natural language processing.

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