Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis

For steganalysis, many studies showed that convolutional neural network (CNN) has better performances than the two-part structure of traditional machine learning methods. Existing CNN architectures use various tricks to improve the performance of steganalysis, such as fixed convolutional kernels, the absolute value layer, data augmentation and the domain knowledge. However, some designing of the network structure were not extensively studied so far, such as different convolutions (inception, xception, etc.) and variety ways of pooling(spatial pyramid pooling, etc.). In this paper, we focus on designing a new CNN network structure to improve detection accuracy of spatial-domain steganography. First, we use $3\times 3$ kernels instead of the traditional $5\times 5$ kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize channel correlation of the residuals, compress the image content and increase the signal-to-noise ratio (between the stego signal and the image signal). Then, we use spatial pyramid pooling (SPP) to aggregate the local features and enhance the representation ability of features by multi-level pooling. Finally, data augmentation is adopted to further improve network performance. The experimental results show that the proposed CNN structure is significantly better than other five methods such as SRM, Ye-Net, Xu-Net, Yedroudj-Net and SRNet, when it is used to detect three spatial algorithms such as WOW, S-UNIWARD and HILL with a wide variety of datasets and payloads.