NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
This challenge provides a benchmark and dataset for improving the robustness of AI-generated image detectors against common image transformations, which is critical for practical deployment in digital forensics.
The NTIRE 2026 challenge aimed to develop robust AI-generated image detectors that can handle real-world transformations like cropping and compression. The winning solutions achieved high ROC AUC scores on a large dataset of 294,500 images from 42 generators, with 36 transformations applied.
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.