The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, 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 on the existing approaches provide informative discussions and future research directions for Deepfake detection.

翻译：互联网上泛滥传播的深度伪造合成内容已对全球政要、名人与普通民众产生了深远的社会影响。本综述从可靠性视角出发，对现有深度伪造检测研究进行了系统性梳理。我们指出当前深度伪造检测领域存在三个面向可靠性的核心挑战：可迁移性、可解释性与鲁棒性。尽管已有大量研究针对这三类挑战提出解决方案，但检测模型的整体可靠性却鲜被考量，导致实际应用乃至法庭审理深度伪造相关案件时缺乏可靠证据。为此，我们引入基于统计随机抽样知识与公开基准数据集的模型可靠性评估指标，用以审视现有检测模型对任意深度伪造嫌疑样本的可靠性。通过本综述筛选出的可靠性达标检测模型，我们进一步开展案例研究以验证涉及不同受害者群体的真实深度伪造案件。对现有方法的综述与实验为深度伪造检测领域提供了建设性讨论与未来研究方向。