Deepfake: definitions, performance metrics and standards, datasets, and a meta-review.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1400024
Enes Altuncu, Virginia N L Franqueira, Shujun Li
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引用次数: 0

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.

Deepfake:定义、性能指标和标准、数据集和元综述。
最近,人工智能(尤其是深度学习)的发展促进了新的逼真合成媒体(视频、图像和音频)的创建和对现有媒体的处理的显著增加,这导致了新术语 "deepfake "的产生。本文以研究文献和英文资源为基础,对深度伪造进行了全面概述,涵盖了这一新兴概念的多个重要方面,包括:(1)不同的定义;(2)常用的性能指标和标准;(3)与深度伪造相关的数据集。此外,本文还报告了对 2020 年以来发表的 15 篇精选 deepfake 相关调查论文的元综述,不仅侧重于上述方面,还分析了主要挑战和建议。我们认为,就所涉及的方面而言,本文是对 deepfake 最全面的综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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