FCD-Net: Learning to Detect Multiple Types of Homologous Deepfake Face Images

Abstract

With the rapid development of artificial intelligence technology, a variety of GAN generated deepfake face images/videos have emerged endlessly. The abuse of deepfake has brought serious negative effects to many industries. Therefore, there is an urgent need to develop advanced methods to combat the abuse of deepfake. As far as we know, there are almost no techniques that can distinguish multiple types of homologous deepfake face images. In this study, we propose a method based on the multi-classification task to address this issue. The proposed method relies on a novel network framework named FCD-Net that consists of the facial synaptic saliency module (FSS), the contour detail feature extraction module (CDFE), and the distinguishing feature fusion module (DFF). Utilizing this method, the imperceptible features introduced by deepfake can be exposed, and the differences caused by different types of deepfake can be distinguished, even if deepfake images are homologous. To test the proposed method and compare it with other SOTA methods, we establish a new homologous dataset named HDFD that contains real face images, entire face synthesis images, face swap images, and facial attribute manipulation images. Among them, the three types of deepfake images are all generated from the same real face images through different deepfake techniques. Abundant experiment results demonstrate that the proposed method has a high-level detection accuracy and relatively strong robustness against content-preserving manipulations. Moreover, the generalization of our method is superior to other SOTA methods.