  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.
