{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6S7ERH7VNB6C5TUCCGL6TNWREP","short_pith_number":"pith:6S7ERH7V","schema_version":"1.0","canonical_sha256":"f4be489ff5687c2ece821197e9b6d123fb8e469cce7bf78197ac85462ead7265","source":{"kind":"arxiv","id":"2509.02859","version":1},"attestation_state":"computed","paper":{"title":"Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Ajinkya Kulkarni, Artem Fedorchenko, Atharva Kulkarni, Benoit Fauve, Damien Lolive, Hoan My Tran, Joonas Kalda, Matthew Magimai Doss, Sandipana Dowerah, Tanel Alum\\\"ae","submitted_at":"2025-09-02T22:11:29Z","abstract_excerpt":"Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocols for reproducibility and transparency. It also includes a leaderboard to compare and rank the system"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.02859","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-09-02T22:11:29Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"2671d90ea580c403f5d50e7c85c3b58373ce79f96f0508a2369b964c44ab1722","abstract_canon_sha256":"c5c16f01a8826e8eef1b9ee8b2c0ca7aa396595a27720aca452d6c7f1be0dd82"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:58.679706Z","signature_b64":"Jvg1JMemlKmRbQtxTX5KzKZ2qaV0+9HR5gpTdlsoPteTdoC4/E60fXWINoLRgLUtYNbxtGUEjTv04mutoR1xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4be489ff5687c2ece821197e9b6d123fb8e469cce7bf78197ac85462ead7265","last_reissued_at":"2026-07-05T12:03:58.679197Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:58.679197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Ajinkya Kulkarni, Artem Fedorchenko, Atharva Kulkarni, Benoit Fauve, Damien Lolive, Hoan My Tran, Joonas Kalda, Matthew Magimai Doss, Sandipana Dowerah, Tanel Alum\\\"ae","submitted_at":"2025-09-02T22:11:29Z","abstract_excerpt":"Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocols for reproducibility and transparency. It also includes a leaderboard to compare and rank the system"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02859","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.02859/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.02859","created_at":"2026-07-05T12:03:58.679263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.02859v1","created_at":"2026-07-05T12:03:58.679263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02859","created_at":"2026-07-05T12:03:58.679263+00:00"},{"alias_kind":"pith_short_12","alias_value":"6S7ERH7VNB6C","created_at":"2026-07-05T12:03:58.679263+00:00"},{"alias_kind":"pith_short_16","alias_value":"6S7ERH7VNB6C5TUC","created_at":"2026-07-05T12:03:58.679263+00:00"},{"alias_kind":"pith_short_8","alias_value":"6S7ERH7V","created_at":"2026-07-05T12:03:58.679263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23201","citing_title":"MixFake: Benchmarking and Enhancing Audio Deepfake Detection in Diverse Real-world Mixed Audio","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2603.27557","citing_title":"A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP","json":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP.json","graph_json":"https://pith.science/api/pith-number/6S7ERH7VNB6C5TUCCGL6TNWREP/graph.json","events_json":"https://pith.science/api/pith-number/6S7ERH7VNB6C5TUCCGL6TNWREP/events.json","paper":"https://pith.science/paper/6S7ERH7V"},"agent_actions":{"view_html":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP","download_json":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP.json","view_paper":"https://pith.science/paper/6S7ERH7V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.02859&json=true","fetch_graph":"https://pith.science/api/pith-number/6S7ERH7VNB6C5TUCCGL6TNWREP/graph.json","fetch_events":"https://pith.science/api/pith-number/6S7ERH7VNB6C5TUCCGL6TNWREP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP/action/storage_attestation","attest_author":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP/action/author_attestation","sign_citation":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP/action/citation_signature","submit_replication":"https://pith.science/pith/6S7ERH7VNB6C5TUCCGL6TNWREP/action/replication_record"}},"created_at":"2026-07-05T12:03:58.679263+00:00","updated_at":"2026-07-05T12:03:58.679263+00:00"}