Towards Benchmarking and Evaluating Deepfake Detection

Abstract

Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos. It is natural to ask which are the top performers among the existing deepfake detection approaches to identify promising research directions and provide practical guidance. Unfortunately, it's difficult to conduct a sound benchmarking comparison of existing detection approaches using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to establish a comprehensive and consistent benchmark, to develop a repeatable evaluation procedure, and to measure the performance of a range of detection approaches so that the results can be compared soundly. A challenging dataset consisting of the manipulated samples generated by more than 13 different methods has been collected, and 11 popular detection approaches (9 algorithms) from the existing literature have been implemented and evaluated with 6 fair-minded and practical evaluation metrics. Finally, 92 models have been trained and 644 experiments have been performed for the evaluation. The results along with the shared data and evaluation methodology constitute a benchmark for comparing deepfake detection approaches and measuring progress.

GAN-Generated Faces Detection: A Survey and New Perspectives

“…The current surveys can help prevent the potential harm caused by the misuse of DeepFakes and ensure that the information presented is accurate and trustworthy [106,50]. Furthermore, other surveys have been conducted with a different perspective compared to existing survey papers, for example, the survey [56] mostly focuses on the audio deepfakes that are overlooked in most of the previous surveys, [74] focus on the audio-visual DeepFakes generation and detection, and the benchmarks [2,65,94,60].…”