{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EEIGBIZSI3OI7MSDNCMQ7ZKKGG","short_pith_number":"pith:EEIGBIZS","schema_version":"1.0","canonical_sha256":"211060a33246dc8fb24368990fe54a319a0bdc05e73605e4b26888b571400932","source":{"kind":"arxiv","id":"2508.15521","version":1},"attestation_state":"computed","paper":{"title":"DualMark: Identifying Model and Training Data Origins in Generated Audio","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Congyi Fan, Dongli Xu, Feiyang Xiao, Haohe Liu, Jian Guan, Qiaoxi Zhu, Xuefeng Yang, Youtian Lin","submitted_at":"2025-08-21T12:49:40Z","abstract_excerpt":"Existing watermarking methods for audio generative models only enable model-level attribution, allowing the identification of the originating generation model, but are unable to trace the underlying training dataset. This significant limitation raises critical provenance questions, particularly in scenarios involving copyright and accountability concerns. To bridge this fundamental gap, we introduce DualMark, the first dual-provenance watermarking framework capable of simultaneously encoding two distinct attribution signatures, i.e., model identity and dataset origin, into audio generative mod"},"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":"2508.15521","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2025-08-21T12:49:40Z","cross_cats_sorted":[],"title_canon_sha256":"2fb6b1183572fc466aaac9083a591b988c24c6df90e8728499b4792639278514","abstract_canon_sha256":"20cf6d38aa8eca590f5b7918c3c45fbe30bf0e1bb2bf17bb3dadb6e593cec904"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:13.837176Z","signature_b64":"UW333wNbx1XJVIJTVKXp9NUDSuKGf7/hL8oT+ZriII6peZHi6F1bkuFiEi32CIm8Iu/IH0ivfME1yIGXLXMQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"211060a33246dc8fb24368990fe54a319a0bdc05e73605e4b26888b571400932","last_reissued_at":"2026-07-05T11:57:13.836670Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:13.836670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DualMark: Identifying Model and Training Data Origins in Generated Audio","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Congyi Fan, Dongli Xu, Feiyang Xiao, Haohe Liu, Jian Guan, Qiaoxi Zhu, Xuefeng Yang, Youtian Lin","submitted_at":"2025-08-21T12:49:40Z","abstract_excerpt":"Existing watermarking methods for audio generative models only enable model-level attribution, allowing the identification of the originating generation model, but are unable to trace the underlying training dataset. This significant limitation raises critical provenance questions, particularly in scenarios involving copyright and accountability concerns. To bridge this fundamental gap, we introduce DualMark, the first dual-provenance watermarking framework capable of simultaneously encoding two distinct attribution signatures, i.e., model identity and dataset origin, into audio generative mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15521","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/2508.15521/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":"2508.15521","created_at":"2026-07-05T11:57:13.836762+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.15521v1","created_at":"2026-07-05T11:57:13.836762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15521","created_at":"2026-07-05T11:57:13.836762+00:00"},{"alias_kind":"pith_short_12","alias_value":"EEIGBIZSI3OI","created_at":"2026-07-05T11:57:13.836762+00:00"},{"alias_kind":"pith_short_16","alias_value":"EEIGBIZSI3OI7MSD","created_at":"2026-07-05T11:57:13.836762+00:00"},{"alias_kind":"pith_short_8","alias_value":"EEIGBIZS","created_at":"2026-07-05T11:57:13.836762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG","json":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG.json","graph_json":"https://pith.science/api/pith-number/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/graph.json","events_json":"https://pith.science/api/pith-number/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/events.json","paper":"https://pith.science/paper/EEIGBIZS"},"agent_actions":{"view_html":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG","download_json":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG.json","view_paper":"https://pith.science/paper/EEIGBIZS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.15521&json=true","fetch_graph":"https://pith.science/api/pith-number/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/graph.json","fetch_events":"https://pith.science/api/pith-number/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/action/storage_attestation","attest_author":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/action/author_attestation","sign_citation":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/action/citation_signature","submit_replication":"https://pith.science/pith/EEIGBIZSI3OI7MSDNCMQ7ZKKGG/action/replication_record"}},"created_at":"2026-07-05T11:57:13.836762+00:00","updated_at":"2026-07-05T11:57:13.836762+00:00"}