Linguistic Steganalysis via Densely Connected LSTM with Feature Pyramid

With the growing attention on multimedia security and rapid development of natural language processing technologies, various linguistic steganographic algorithms based on automatic text generation technology have been proposed increasingly, which brings great challenges in maintaining security of cyberspace. The prevailing linguistic steganalysis methods based on neural networks only conduct linguistic steganalysis with feature vectors from last layer of neural network, which may be insufficient for neural linguistic steganalysis. In this paper, we propose a neural linguistic steganalysis scheme based on densely connected Long short-term memory networks (LSTM) with feature pyramids which can incorporate more low level features to detect generative text steganographic algorithms. In the proposed framework, words in text are firstly mapped into semantic space with a hidden representation for better exploitation of the semantic features. Then, stacked bidirectional Long short-term memory networks are ultilized to extract different levels of semantic features. In order to incorporate more low level features from neural networks, we introduced two components: dense connections and feature pyramids to enhance the low level features in feature vectors. Finally, the semantic features from all levels are fused and we use a sigmoid layer to categorize the input text as cover or stego. Experiments showed that the proposed scheme can achieve the state-of-the-art results in detecting recently proposed linguistic steganographic algorithms.