Deepfake swapped face detection based on double attention - Xiaojuan GONG; Tianqiang HUANG; Bin WENG; Feng YE; Chao XU; Lijun YOU (网络与信息安全学报, 2021)
In view of the existing Deepfake detection algorithms, such problems as low accuracy and poor interpretability are common.A neural network model combining the double attention was proposed, which used channel attention to capture the abnormal features of false faces and combined the location of spatial attention to focus the abnormal features.To fully learn the contextual semantic information of the abnormal part of the false face, so as to improve the effectiveness and accuracy of face changing detection.In addition, the decision-making area of real and fake faces was shown effectively in the form of thermal diagram, which provided a certain degree of explanation for the face exchange detection model.Experiments on FaceForensics ++ open source data set show that the detection accuracy of proposed method is superior to MesoInception, Capsule-Forensics and XceptionNet.