In our work, we mix central difference convolution and vanilla convolution(CDC-Mix) after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC-Mix, we propose a separable convolution (SeparableCDC-Mix). The proposed method consists of three parts:
- CDC-Mix and SeparableCDC-Mix are used to extract the gradient information and texture features;
- CDCM is used to extract the multi-scale information of the image;
- multi-scale fusion module(MS-Fusion) is used to fuse the multi-scale information from different locations of the network.
A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods.
If you have used our code, please cite the paper.
He D, Jiang Q, Jin X, et al. MCDC‐Net: Multi‐scale forgery image detection network based on central difference convolution[J]. IET Image Processing, 2023.