Deepfake Detection: A Comprehensive Survey from the Reliability Perspective
This work addresses the need for reliable Deepfake detection to support real-life applications and legal cases, but it is incremental as it builds on existing studies with a new reliability focus.
The survey reviews Deepfake detection methods from a reliability perspective, identifying transferability, interpretability, and robustness as key challenges, and introduces a model reliability metric using statistical sampling and benchmark datasets to assess detection models on arbitrary Deepfake suspects.
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.