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.

中文翻译：

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Deepfake 检测：从可靠性角度进行全面调查


互联网上流传的如雨后春笋般涌现的 Deepfake 合成材料对全球政治家、名人和个人产生了深远的社会影响。在这项调查中，我们从可靠性的角度对现有的 Deepfake 检测研究进行了全面审查。我们确定了当前 Deepfake 检测领域的三个以可靠性为导向的研究挑战：可转移性、可解释性和稳健性。此外，虽然经常针对这三个挑战提出解决方案，但检测模型的一般可靠性几乎没有被考虑，导致在实际使用中缺乏可靠的证据，甚至在法庭上对 Deepfake 相关案件的起诉也缺乏可靠证据。因此，我们使用统计随机抽样知识和公开可用的基准数据集引入了模型可靠性研究指标，以审查现有检测模型对任意 Deepfake 候选嫌疑人的可靠性。进一步执行案例研究，以证明现实生活中的 Deepfake 案例的合理性，包括本调查中回顾的可靠合格检测模型，包括不同的受害者群体。对现有方法的回顾和实验为 Deepfake 检测提供了信息丰富的讨论和未来的研究方向。