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.

本文综述了深度伪造技术 (deepfake) 的多个重要方面，包括不同的定义、常用的性能指标和标准以及深度伪造相关的数据集、挑战、竞赛和基准。与此同时，还对2020和2021年发表的12篇综述论文进行了元评论。本文是深度伪造领域方面最全面的综述，是第一篇涵盖英文和中文文献和资源的综述。

Deepfake: 定义、性能指标和标准、数据集和基准，以及元回顾