Generalized Design Choices for Deepfake Detectors
The study systematically tested various design choices for deepfake detection models across different training and testing scenarios using AI-GenBench. It found that specific data augmentation techniques, like three JPEG compression passes, significantly improve generalization to new generators. The research also identified optimal inference strategies—such as resizing the full image—and effective continual learning methods to maintain high accuracy over time.