🤖 AI Summary
This work addresses the robustness challenges in detecting AI-generated images under real-world conditions, where rapid model evolution and diverse distortions degrade detector performance. To this end, the authors propose a three-way heterogeneous ensemble architecture that introduces structured heterogeneity across backbone networks (DINOv3 and MetaCLIP2), training strategies, and input resolutions. The framework incorporates a high-resolution, fine-grained forensic branch alongside progressive data augmentation and features a lightweight dual-gating mechanism for adaptive logit-space fusion. Evaluated in the NTIRE 2026 Robust AIGC Detection Challenge, the method secured fourth place and achieves state-of-the-art performance across multiple benchmarks, demonstrating significantly enhanced generalization and robustness.
📝 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.