{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UMMVLVIOGE3ZQIMIIUUQLVSCKQ","short_pith_number":"pith:UMMVLVIO","schema_version":"1.0","canonical_sha256":"a31955d50e3137982188452905d642540930b6c0e29fa141a6c7c920c783663d","source":{"kind":"arxiv","id":"2509.00186","version":1},"attestation_state":"computed","paper":{"title":"Generalizable Audio Spoofing Detection using Non-Semantic Representations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Arnab Das, Carlos Franzreb, Sebastian M\\\"oller, Tim Herzig, Tim Polzehl, Yassine El Kheir","submitted_at":"2025-08-29T18:37:57Z","abstract_excerpt":"Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. Extensive experiments have been performed to find suitable non-semantic features using TRILL and TRIL"},"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":"2509.00186","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2025-08-29T18:37:57Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"7603d2e0946784673ffa4dfdfbcefda920434a20c9f9ff8ca5bf80337bc0532c","abstract_canon_sha256":"1709c080152449c7f4d6c6cf1302d7e281852a2b4ef2c88163f692d7621f8bcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:10.957243Z","signature_b64":"E6L1ToUFyc1ySis/rCKdeZkJSUINeS4YhJtwHoLjc6PerqgExvv3gncwTrDaqNoVOX0Rf/XLSio/jxkED/FgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a31955d50e3137982188452905d642540930b6c0e29fa141a6c7c920c783663d","last_reissued_at":"2026-07-05T12:02:10.956774Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:10.956774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalizable Audio Spoofing Detection using Non-Semantic Representations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Arnab Das, Carlos Franzreb, Sebastian M\\\"oller, Tim Herzig, Tim Polzehl, Yassine El Kheir","submitted_at":"2025-08-29T18:37:57Z","abstract_excerpt":"Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. Extensive experiments have been performed to find suitable non-semantic features using TRILL and TRIL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00186","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/2509.00186/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":"2509.00186","created_at":"2026-07-05T12:02:10.956834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.00186v1","created_at":"2026-07-05T12:02:10.956834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00186","created_at":"2026-07-05T12:02:10.956834+00:00"},{"alias_kind":"pith_short_12","alias_value":"UMMVLVIOGE3Z","created_at":"2026-07-05T12:02:10.956834+00:00"},{"alias_kind":"pith_short_16","alias_value":"UMMVLVIOGE3ZQIMI","created_at":"2026-07-05T12:02:10.956834+00:00"},{"alias_kind":"pith_short_8","alias_value":"UMMVLVIO","created_at":"2026-07-05T12:02:10.956834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00186","citing_title":"Generalizable Audio Spoofing Detection using Non-Semantic Representations","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ","json":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ.json","graph_json":"https://pith.science/api/pith-number/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/graph.json","events_json":"https://pith.science/api/pith-number/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/events.json","paper":"https://pith.science/paper/UMMVLVIO"},"agent_actions":{"view_html":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ","download_json":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ.json","view_paper":"https://pith.science/paper/UMMVLVIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.00186&json=true","fetch_graph":"https://pith.science/api/pith-number/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/graph.json","fetch_events":"https://pith.science/api/pith-number/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/action/storage_attestation","attest_author":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/action/author_attestation","sign_citation":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/action/citation_signature","submit_replication":"https://pith.science/pith/UMMVLVIOGE3ZQIMIIUUQLVSCKQ/action/replication_record"}},"created_at":"2026-07-05T12:02:10.956834+00:00","updated_at":"2026-07-05T12:02:10.956834+00:00"}