Review of Deep Network Generative Fake Face Detection Methods
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.
fake face detectiongenerative fake faceface imageface videodeep network
万方数据
维普
