Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks

With the rapid development of deep learning technology, videos with changed faces generated by deep neural networks (i.e., Deepfake videos) become more and more indistinguishable. As a result, the threat raised by Deepfake videos becomes greater and greater. In literature, there are some convolutional neural networks-based detection algorithms for fake face videos. Although those algorithms perform well when the training set and the testing set are from the same dataset, their performance could deteriorate dramatically in cross-dataset scenario where the training and the testing sets are from different sources. Motivated by the fabrication course of fake face videos, this article attempts to solve the problem of fake faces detection with the way of image splicing detection. A neural network borrowed from image segmentation is adopted for predicting  the tampered face area from which a tampering mask is obtained through denoising and thresholding the probability map. Using the prior knowledge of face tampering that the changing of face mainly happens in face region, a new way is proposed to determine the Face-Intersection over Union (Face-IoU) and to further improve the ratio calculation method. The Face-Intersection over Union with Penalty (Face-IoUP) is used as the classification criterion for deepfake video detection. The proposed method is impletmented using three basic image segmentation neural networks separately and is tested them on datasets of TIMIT, FaceForensics++, Fake Face in the Wild(FFW). Compared with current methods in literature, the HTER (Half Total Error Rate) in cross-dataset test decreases significantly while the detection accuracy in intra-dataset test keeps high. For the Deep Fake Detection(DFD) dataset with higher synthesis quality, the proposed method still performs very well. Experimental results validate the proposed method and demonstrate its good generality.