HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild

• URL: http://arxiv.org/abs/2604.03555v1
• Date: Sat, 04 Apr 2026 02:46:36 GMT
• Title: HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild
• Abstract summary: HEDGE is a Heterogeneous Ensemble for Detection of AI-GEnerated images.<n> RouteA progressively constructs DINOv3-based detectors through staged data expansion and augmentation escalation.<n> RouteB incorporates a higher-resolution branch for fine-grained forensic cues, and RouteC adds a MetaCLIP2-based branch for backbone diversity.
• Score: 17.30735058274559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Heterogeneous Ensemble for Detection of AI-GEnerated images, that introduces complementary detection routes along three axes: diverse training data with strong augmentation, multi-scale feature extraction, and backbone heterogeneity. Specifically, Route~A progressively constructs DINOv3-based detectors through staged data expansion and augmentation escalation, Route~B incorporates a higher-resolution branch for fine-grained forensic cues, and Route~C adds a MetaCLIP2-based branch for backbone diversity. All outputs are fused via logit-space weighted averaging, refined by a lightweight dual-gating mechanism that handles branch-level outliers and majority-dominated fusion errors. HEDGE achieves 4th place in the NTIRE 2026 Robust AI-Generated Image Detection in the Wild Challenge and attains state-of-the-art performance with strong robustness on multiple AIGC image detection benchmarks.

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