Multi-Frequency Residual Convolutional Neural Network for Steganalysis of Color Images

Current steganalyzers based on deep learning mostly adopt wider or deeper designs to improve detection performance. However, an overly complex network increases the training cost and is not conducive to its expansion and optimization. Moreover, steganalysis pays more attention to high-frequency information corresponding to the image texture. However, the deeper the network, the more likely it is to learn low-frequency information corresponding to the image content, which is inconsistent with the goal of steganalysis. In response to these problems, a multi-frequency residual deep convolutional neural network for steganalysis of color images called MFRNet is proposed in this paper. We apply the idea of multi-frequency analysis to steganography detection for the first time, effectively controlling the network scale. By designing columns of different depths, it can learn different frequency components of steganographic noise at the same time. The detection performance is better than the existing networks that only learn a single frequency component of steganographic noise at the same depth. Therefore, it can achieve a good detection performance with a lighter architecture. In addition, by designing residual basic blocks with different residual shortcuts, different scales of steganographic noise residuals can be calculated at the same time, which can effectively suppress the interference of image content, so as to better reduce the impact of steganography algorithm mismatch and payload mismatch than the existing methods. The experimental results on PPG-LIRMM-COLOR showed that the proposed MFRNet outperformed the state-of-the-art model WISERNet.