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.
翻译:在互联网上散发的蘑菇Deepfake合成材料对政界人士、名人和地球上的每一个人产生了严重的社会影响。在这次调查中,我们从可靠性的角度对现有的Deepfake探测研究进行彻底审查。目前Deepfake探测研究领域的可靠性研究挑战分为三个方面,即可转移性、可解释性和稳健性。虽然对这三项挑战经常提出解决办法,但很少考虑探测模型的一般可靠性问题,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,我们采用一个模型可靠性研究指标,利用统计随机抽样知识和公开可得的基准数据集来审查关于任意Deepfake候选嫌疑人的现有探测模型的可靠性。进一步开展了个案研究,以便在本次调查中审查的可靠合格探测模型的帮助下,证明包括不同受害人群体在内的真实生活中的Deepfake案件是正当的。审查和试验了现有方法,为Deepfake探测提供了内容丰富的讨论和今后的研究方向。