{"paper":{"title":"RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"The RADAR Challenge shows audio deepfake detectors still fail under common media changes and multiple languages.","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Hieu-Thi Luong, Ivan Kukanov, Kong Aik Lee, Xuechen Liu, Zheng Xin Chai","submitted_at":"2026-05-10T14:29:35Z","abstract_excerpt":"RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world audio distribution pipelines, including compression, resampling, noise, and reverberation. It consists of two phases: an English development phase with labeled data for analysis and paper writing, and a multilingual evaluation phase containing more than 100,000 utterances in English, Singapore English, Mandarin Chinese, Taiwanese Mandarin, Japanese, and Vietnamese. Systems are evaluated using equal error rate (EER) for"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The reported results highlight the remaining challenges of robust audio deepfake detection under multilingual and media-transformed conditions.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The constructed dataset and chosen media transformations accurately represent the distortions that occur in real-world audio distribution pipelines.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The RADAR Challenge 2026 provides a multilingual benchmark for audio deepfake detection under media transformations and finds that robust performance remains an open problem.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The RADAR Challenge shows audio deepfake detectors still fail under common media changes and multiple languages.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2a80654e888a417b2cecaad2a1c2a8e0203b9baddfc8de6e652cf00a1256e2af"},"source":{"id":"2605.09568","kind":"arxiv","version":2},"verdict":{"id":"fb81893f-96be-42ea-a279-2e62b74cb186","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T02:29:46.008558Z","strongest_claim":"The reported results highlight the remaining challenges of robust audio deepfake detection under multilingual and media-transformed conditions.","one_line_summary":"The RADAR Challenge 2026 provides a multilingual benchmark for audio deepfake detection under media transformations and finds that robust performance remains an open problem.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The constructed dataset and chosen media transformations accurately represent the distortions that occur in real-world audio distribution pipelines.","pith_extraction_headline":"The RADAR Challenge shows audio deepfake detectors still fail under common media changes and multiple languages."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.09568/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T16:39:41.478552Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T13:01:17.497986Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T10:08:52.630011Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"5eb6124285c1808a35766197482174c1c423377e3c6c5041cfb7f42201146bec"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"411d9362d79cd96338fe31aca2d981e2102c1cf92176ac4b163b3d9ec96d5af6"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}