Altuncu Enes, Franqueira Virginia N L, Li Shujun
Institute of Cyber Security for Society (iCSS) & School of Computing, University of Kent, Canterbury, United Kingdom.
Front Big Data. 2024 Sep 4;7:1400024. doi: 10.3389/fdata.2024.1400024. eCollection 2024.
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.
人工智能尤其是深度学习的最新进展,极大地推动了逼真的新型合成媒体(视频、图像和音频)的创造以及对现有媒体的操控,由此催生了“深度伪造”这一新术语。基于英文的研究文献和资源,本文全面概述了深度伪造,涵盖了这一新兴概念的多个重要方面,包括(1)不同定义,(2)常用的性能指标和标准,以及(3)与深度伪造相关的数据集。此外,本文还对2020年以来发表的15篇选定的与深度伪造相关的综述论文进行了元综述,不仅关注上述方面,还对关键挑战和建议进行了分析。我们认为,就所涵盖的方面而言,本文是对深度伪造最为全面的综述。