A Convolution Neural Network Based on Residual Learning for Image Steganalysis

Abstract

Image steganalysis is a very important technology for forensics. Recent studies show that the idea of steganalysis based on Convolutional Neural Network (CNN) is feasible. In this paper, we propose a novel digital image steganalysis model based on CNN. Compared with the existing CNN-based methods, the CNN model proposed to this paper has two characteristics. First, in the front of the network, high-pass filter in SRM is used to initialize the convolution kernels, which is beneficial to learning steganography noise in the image. Second, in the middle of the network, the residual learning mechanism is used to enhance the convergence speed and stability of the network. Experiments on the standard data set show that the proposed CNN model can detect S-UNIWARD steganography algorithm with high accuracy.