Framework identifies which AI model generated videos

The rise of hyper-realistic synthetic videos created by generative AI has outpaced the ability of existing detection tools to handle the threat. Binary real/fake detectors, which simply classify whether a video is authentic or synthetic, lack the granularity needed for forensic investigation and regulatory enforcement. SAGA addresses this gap by going beyond detection to identify the source—not just that a video is fake, but which specific model, version, and development team generated it. This shift from binary classification to multi-level attribution reflects a maturation in how the field thinks about synthetic media accountability.

The framework's technical innovations center on two key advances. First, a novel video transformer architecture leverages features from a robust vision foundation model to capture spatio-temporal artifacts—the telltale traces left by different generators across both space and time. Second, the data-efficient pretrain-and-attribute strategy allows SAGA to match the performance of fully supervised systems while using only 0.5% of the labeled data per class, a significant practical advantage for scaling deployment across many generator models. The introduction of Temporal Attention Signatures provides interpretability by visualizing the learned temporal differences that distinguish one generator from another, offering the first transparent explanation for why the framework's attributions are possible.