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 paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in three aspects, namely, transferability, interpretability, and reliability. While the transferability and interpretability challenges have both been frequently discussed and attempted to solve with quantitative evaluations, the reliability issue 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 conduct a model reliability study scheme using statistical random sampling knowledge and the publicly available benchmark datasets to qualitatively validate the detection performance of the existing models on arbitrary Deepfake candidate suspects. A barely remarked systematic data pre-processing procedure is demonstrated along with the fair training and testing experiments on the existing detection models. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of reliably qualified detection models. The model reliability study provides a workflow for the detection models to act as or assist evidence for Deepfake forensic investigation in court once approved by authentication experts or institutions.
翻译:在互联网上散发的蘑菇Deepfake合成材料给政界人士、名人和地球上的每一个人带来了严重的社会影响;在本文件中,我们根据Deepfake探测研究的发展史对现有模型的现有模型进行彻底审查,并从三个方面,即可转移性、可解释性和可靠性,界定Deepfake探测的研究挑战;虽然经常讨论并试图通过定量评估来解决可转移性和可解释性挑战,但可靠性问题却很少得到考虑,导致实际使用中缺乏可靠的证据,甚至在法庭上对Deepfake相关案件进行起诉;因此,我们利用统计随机抽样知识和公开可得的基准数据集,对现有的Deepfake候选嫌疑人模型的探测性能进行定性验证,同时对现有探测模型进行公平的培训和测试试验;进一步开展案例研究,为真实生活深海案件(包括不同的受害人群体)提供正当理由,帮助进行可靠的检测模型;因此,模型可靠性研究为探测模型提供了一个工作流程,一旦获得核准,或由法庭认证,则作为证据,或协助进行深藏法证机构采取行动。