A Generalizable and Explainable Framework for Synthetic Video Detection Using First-Digit Gradient Statistics
This work introduces a generalizable method to detect AI-generated videos by analyzing first-digit statistics from Sobel gradients. It combines spatial and temporal features derived from ten image channels into an 800-dimensional vector. This framework successfully identifies synthetic video artifacts across various generators, proving the signal is universal and not tied to specific video formats.