标杆管理
计算机科学
声誉
数据科学
政治学
业务
法学
营销
作者
Jia Wen Seow,Mei Kuan Lim,Raphaël C.‐W. Phan,Joseph K. Liu
出处
期刊:Neurocomputing
[Elsevier BV]
日期:2022-09-28
卷期号:513: 351-371
被引量:69
标识
DOI:10.1016/j.neucom.2022.09.135
摘要
When used maliciously, deepfake can pose detrimental implications to political and social forces including reducing public trust in institutions, damaging the reputation of prominent individuals, and influencing public opinions. As there is currently no specific law to address deepfakes, thus deepfake detection, which is an action to discriminate pristine media from deepfake media, plays a vital role in identifying and thwarting deepfake. This paper provides readers with a comprehensive and easy-to-understand state-of-the-art related to deepfake generation and detection. Specifically, we provide a synthesized overview and recent progress in deepfakes by categorizing our review into deepfake generation and detection. We underline publicly available deepfake generation tools and datasets for benchmarking. We also provide research insights, discuss existing gaps, and present trends for future research to facilitate the development of deepfake research.
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