Deep visual identity forgery and detection

Abstract

With the wide application of deep learning techniques in video and image generation, the quality of visual identity forgery, especially face forgery, is becoming increasingly high. The detection of visual identity forgery has been a hot issue because of its important influence on both national security and social stability. In this paper, we introduce recent researches on deep visual identity forgery from target-specific face forgery and target-generic face forgery. We further summarize the techniques of detecting visual identity forgery from multiple categories, including the spatial clue based methods, the temporal clue based method, the techniques for generalizable forgery detection and spoofing forgery detection models. We later present the public datasets and performance of representative approaches on these datasets. Finally, the issues and challenges in existing research are discussed.