关键词: 隐写分析, 神经网络, 密集连接, 梯度消失

Abstract: In view of the shortcomings of the traditional steganalysis technology for the feature set demanding higher and higher requirements, a Steganalysis-Densely Connected Convolutional Network（S-DCCN） model is constructed for image steganalysis to avoid the feature extraction and improves the efficiency of steganalysis. Firstly, a High-Pass Filter（HPF） filter is added in front of the network layer to speed up model training. And the filtered image enters two convolution layers for feature extraction. After the convolution layer using five groups of dense connection module to solve the network deepen gradient disappear dense connection between modules to control the network the width of the transition layer. Experimental results show that compared with the traditional image steganalysis algorithm and the convolutional neural network technology, the proposed model can effectively improve the accuracy and generalization performance of steganalysis.

Key words: steganalysis, neural networks, dense connectivity, gradient vanishing