ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis

Recent steganalytic schemes reveal embedding traces in a promising way by using convolutional neural networks (CNNs). However, further improvements, such as exploring complementary data processing operations and using wider structures, were not extensively studied so far. In this letter, we design a new CNN in these aspects in order to better capture embedding artifacts. Specifically, on the one hand, we propose to process information diversely with a module called diverse activation module. On the other hand, we build a wide structure with parallel subnets using several filter groups for preprocessing. To accelerate the training process, we pretrain the subnets independently. Extensive experiments show that the proposed method is effective in detecting content-adaptive steganographic schemes.