AUTHOR=Altuncu Enes , Franqueira Virginia N. L. , Li Shujun TITLE=Deepfake: definitions, performance metrics and standards, datasets, and a meta-review JOURNAL=Frontiers in Big Data VOLUME=7 YEAR=2024 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1400024 DOI=10.3389/fdata.2024.1400024 ISSN=2624-909X ABSTRACT=

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term “deepfake.” Based on both the research literature and resources in English, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including (1) different definitions, (2) commonly used performance metrics and standards, and (3) deepfake-related datasets. In addition, the paper also reports a meta-review of 15 selected deepfake-related survey papers published since 2020, focusing not only on the mentioned aspects but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of the aspects covered.