Statistical Analysis Outperforms Deep Learning in Deepfake Detection

This research reveals that deepfake detection doesn't require expensive neural networks—statistical analysis of image properties like sensor noise and frequency patterns achieves 92.9% ROC-AUC with just 20ms inference and complete interpretability. The findings challenge the assumption that deep learning is mandatory for synthetic media detection, offering practitioners a faster, more explainable alternative that works without GPU infrastructure. A hybrid approach combining statistical screening with deep models suggests the future of production deepfake detection systems.