论文标题
DeepFake：定义，性能指标和标准，数据集和基准，以及元评论
Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review
论文作者
论文摘要
AI的最新进展，尤其是深度学习，已导致创建新的现实合成媒体（视频，图像和音频）以及对现有媒体的操纵的创建大幅度增加，这导致了新术语“ Deepfake”的创建。基于英语和中文中的研究文献和资源，本文概述了深层的概述，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，以及3）与Deepfake相关的数据集，与DeepFake相关的数据集，挑战，挑战，竞争和基准标记。此外，该论文还报告了2020年和2021年发表的12项与DeepFake相关的调查论文的元评估，不仅关注上述方面，而且集中在对关键挑战和建议的分析上。我们认为，就涵盖的各个方面而言，本文是对深层的最全面评论，也是第一个涵盖英语和中国文学和资源的文章。
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 and in Chinese, 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, challenges, competitions and benchmarks. In addition, the paper also reports a meta-review of 12 selected deepfake-related survey papers published in 2020 and 2021, 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 aspects covered, and the first one covering both the English and Chinese literature and sources.
