Deep convolutional neural network-based feature extraction for steganalysis of content-adaptive JPEG steganography

Abstract

Deep learning-based steganalysis techniques have significantly progressed in recent years. Compared with typical steganalysis methods using rich model features and classifiers, deep learning-based steganalysis methods usually train a deep convolutional neural network (CNN) as the steganography detector. One advantage of a deep CNN is the strong capability for discriminative feature learning. The input feature map of the fully connected layer is the steganalysis feature learned using the deep CNN. We can, therefore, extract the steganalysis feature based on the trained deep CNN. Compared with steganalysis features constructed by hand, the extraction of steganalysis features using a deep CNN can take advantage of the strong feature-learning capability of such a network. Therefore, two types of typical steganalysis frameworks are first compared. Then, a deep CNN for steganalysis feature learning is constructed, and the setting of the image preprocessing layer is discussed. Next, the detection performances of the different learned steganalysis features are compared. Finally, the detailed extraction process of the proposed learned steganalysis feature is described. The experiment results show that the proposed steganalysis method, combining learned and handcrafted steganalysis features, can significantly improve the detection performance for content-adaptive JPEG steganography.