Scaling Laws for Deepfake Detection
This research constructed ScaleDF, a massive dataset for deepfake detection, to study scaling laws. It found that detection error follows predictable power-law relationships based on the number of real domains or deepfake methods tested. This reveals how performance scales with data and method diversity.