计算机科学
可解释性
可靠性(半导体)
可转让性
数据科学
稳健性(进化)
公制(单位)
透视图(图形)
风险分析(工程)
人工智能
机器学习
医学
功率(物理)
生物化学
物理
化学
运营管理
罗伊特
量子力学
经济
基因
作者
Tianyi Wang,K. P. Chow,Xiaojun Chang,Yinglong Wang
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:4
标识
DOI:10.48550/arxiv.2211.10881
摘要
The mushroomed Deepfake synthetic materials circulated on the internet have raised serious social impact to politicians, celebrities, and every human being on earth. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. Reliability-oriented research challenges of the current Deepfake detection research domain are defined in three aspects, namely, transferability, interpretability, and robustness. While solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments upon the existing approaches provide informative discussions and future research directions of Deepfake detection.
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