MCDC-Net: Multi-scale forgery image detection network based on central difference convolution

Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC-Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC-Mix, a separable convolution (SeparableCDC-Mix) is proposed. The proposed method consists of three parts: (1) CDC-Mix and SeparableCDC-Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi-scale information of the image; (3) 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. We propose SeparableCDC-Mix to learn the image features. In addition, CDCM is introduced to extract the multi-scale information of the image. To organically integrate multi-scale features extracted by CDCM, we propose an MS-Fusion module.image