挑战真实场景下生成图像的鲁棒检测，推动对抗多重图像变形的识别技术。
NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
- 构建包含42种生成器的超大规模数据集，覆盖多种真实变换。
- 模型需在36种图像处理下保持高鲁棒性，最终平均ROC AUC达0.95以上。
- 适合关注生成内容安全与真实世界部署的AI研究人员。
本文概述了与CVPR 2026 NTIRE研讨会联合举办的NTIRE 2026挑战赛——《野外环境下的鲁棒AI生成图像检测》。该挑战旨在开发能够在实际场景中区分真实图像与生成图像的检测模型：图像常经裁剪、缩放、压缩、模糊等处理，因此检测模型需具备对这些变换的鲁棒性。挑战基于一个新数据集，包含108,750张真实图像和185,750张由42种生成器（涵盖多种开源与闭源模型及架构）生成的图像，并施加36种图像变换。方法在完整测试集上以ROC AUC进行评估，包括已变换与未变换图像。共有511名参与者注册，20支团队提交有效最终方案。本报告全面介绍挑战背景、参赛方案，可为提升检测模型对真实世界变换的鲁棒性提供重要参考。
原文摘要 · Abstract (English)
This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal of this challenge was to develop detection models capable of distinguishing real images from generated ones in realistic scenarios: the images are often transformed (cropped, resized, compressed, blurred) for practical usage, and therefore, the detection models should be robust to such transformations. The challenge is based on a novel dataset consisting of 108,750 real and 185,750 AI-generated images from 42 generators comprising a large variety of open-source and closed-source models of various architectures, augmented with 36 image transformations. Methods were evaluated using ROC AUC on the full test set, including both transformed and untransformed images. A total of 511 participants registered, with 20 teams submitting valid final solutions. This report provides a comprehensive overview of the challenge, describes the proposed solutions, and can be used as a valuable reference for researchers and practitioners in increasing the robustness of the detection models to real-world transformations.
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