DeepFake Detection with Multi-View Fusion and Graph Convolutional Network

Nowadays, massive amounts of facial images have been tampered with and then widely spread through social networks. Many studies have developed algorithms for frame-level DeepFake detection. However, they have low robustness due to their focus on tamper-independent features during training. To this end, we propose a framework, namely MIF-Net, based on multi-information fusion for robust frame-level DeepFake detection. Specifically, key landmarks and the facial area are first detected in the original frame. Then, the graph convolutional network constructs biometric information from these landmarks. Meanwhile, the facial region is processed into multi-view inputs by noise and edge enhancement algorithms. Finally, these products are encoded as high-level features and classified as real or fake. Five benchmark datasets are utilized for testing our model through within-dataset and cross-dataset validations. Extensive experiment results demonstrate that our proposed MIF-Net is robust and has advantages over peer algorithms.