Detecting facial manipulated videos based on set convolutional neural networks

With the boom of artificial intelligence, facial manipulation technology is becoming more simple and more numerous. At the same time, the technology also has a large and profound negative impact on face forensics, such as Deepfakes. In this paper, in order to aggregate multiframe features to detect facial manipulation videos, we solve facial manipulated video detection from set perspective and propose a novel framework based on set, which is called set convolutional neural network (SCNN). Three instances of the proposed framework SCNN are implemented and evaluated on the Deepfake TIMIT dataset, FaceForensics++ dataset and DFDC Preview datset. The results show that the method outperforms previous methods and can achieve state-of-the-art performance on both datasets. As a perspective, the proposed method is a fusion promotion of single-frame digital video forensics network.