Fast and Effective Global Covariance Pooling Network for Image Steganalysis
Recently, deep learning based methods have achieved superior performance compared to conventional methods based on hand-crafted features in image steganalysis. However, most modern methods are usually quite time consuming. For instance, it takes over 3 days to train a state-of-the-art neural network, i.e. SRNet [3] in our experiments. In this paper, therefore, we propose a fast yet very effective convolutional neural network (CNN) for image steganalysis in spatial domain. To make a good tradeoff between training time and performance, we carefully design the architecture of the proposed network according to our extensive experiments. In addition, we first introduce the global covariance pooling into steganalysis to exploit the second-order statistic of high-level features for further improving the performance. Experimental results show that the proposed network can outperform the current best one, while its training time is significantly reduced.
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