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REVIEW article

Front. Big Data
Sec. Cybersecurity and Privacy
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1400024
This article is part of the Research Topic Cybersecurity and Artificial Intelligence: Advances, Challenges, Opportunities, Threats View all 5 articles

Deepfake: Definitions, Performance Metrics and Standards, Datasets, and a Meta-Review

Provisionally accepted
Enes Altuncu Enes Altuncu Virginia N. Franqueira Virginia N. Franqueira Shujun Li Shujun Li *
  • University of Kent, Canterbury, Kent, United Kingdom

The final, formatted version of the article will be published soon.

    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.

    Keywords: Deepfake, Survey, definition, Datasets, Standards, Performance metrics

    Received: 12 Mar 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Altuncu, Franqueira and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shujun Li, University of Kent, Canterbury, CT2 7NZ, Kent, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.