With the rapid spread of deep network generated fake face technology,criminals perpetrate telecom fraud,manipulate public opinion,and disseminate obscenity by forging face images and videos.How to effi-ciently and accurately detect fake faces from massive data has become a research focus.In this review,we sys-tematically summarize,analyze and compare the current deep network generative forgery face detection meth-ods from two fields:generative forgery face image and generative forgery face video.For the forged face im-ages,the detection methods are introduced in detail from five categories:digital image processing foundation,deep feature extraction,spatial domain feature analysis,multi-feature fusion analysis and fingerprint detection.The detection methods of fake face videos are also discussed from four categories:physiological signals,iden-tity information,multi-modal and spatio-temporal inconsistency.The analysis shows that the generalization ability of the current deep network generative fake face detection method needs to be improved.In future re-search,we should focus on improving the cross-dataset generalization ability,accuracy and practicality of the model,so as to better prevent the spread of false information,protect personal privacy and maintain network security environment.
关键词
伪造人脸检测/生成式伪造人脸/人脸图像/人脸视频/深度网络
Key words
fake face detection/generative fake face/face image/face video/deep network
维普
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