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

Autor:	Enes Altuncu, Franqueira, Virginia N. L., Shujun Li
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (cs.LG)
Zdroj:	Web of Science
DOI:	10.48550/arxiv.2208.10913
Popis:	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. Comment: 31 pages; study completed by end of July 2021
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb68c22bb12f72a35a0f8273f5be6400 Zobrazit plný text záznamu