{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:C2ME4PET2AHCOBRBPWCT2MPNZQ","short_pith_number":"pith:C2ME4PET","schema_version":"1.0","canonical_sha256":"16984e3c93d00e2706217d853d31edcc247601f1b0cc559f5ec0dedba9c80650","source":{"kind":"arxiv","id":"2405.15655","version":2},"attestation_state":"computed","paper":{"title":"HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Pengyang Huang, Zhisheng Zhang","submitted_at":"2024-05-24T15:49:00Z","abstract_excerpt":"In recent years, the remarkable advancements in deep neural networks have brought tremendous convenience. However, the training process of a highly effective model necessitates a substantial quantity of samples, which brings huge potential threats, like unauthorized exploitation with privacy leakage. In response, we propose a framework named HiddenSpeaker, embedding imperceptible perturbations within the training speech samples and rendering them unlearnable for deep-learning-based speaker verification systems that employ large-scale speakers for efficient training. The HiddenSpeaker utilizes "},"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":"2405.15655","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-05-24T15:49:00Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"dcd8ce7627014e19a1e7fdbbbda63e09fcf234e57461a6d2f06e40d22d992a40","abstract_canon_sha256":"11dfec4992d5f94bb6e0ded7818dc9fd355bf174dee915ae3ab1f70c9ced1130"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:06:05.860502Z","signature_b64":"hoBBVDsN91Fh0bGvK+m3wc6TRPJPGUe5OKvIDVnSlSYot3AhaU9dJqjg/hTkqIpLFa4UPA6NkzMg6cq6z1LUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16984e3c93d00e2706217d853d31edcc247601f1b0cc559f5ec0dedba9c80650","last_reissued_at":"2026-07-05T09:06:05.860013Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:06:05.860013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Pengyang Huang, Zhisheng Zhang","submitted_at":"2024-05-24T15:49:00Z","abstract_excerpt":"In recent years, the remarkable advancements in deep neural networks have brought tremendous convenience. However, the training process of a highly effective model necessitates a substantial quantity of samples, which brings huge potential threats, like unauthorized exploitation with privacy leakage. In response, we propose a framework named HiddenSpeaker, embedding imperceptible perturbations within the training speech samples and rendering them unlearnable for deep-learning-based speaker verification systems that employ large-scale speakers for efficient training. The HiddenSpeaker utilizes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15655","kind":"arxiv","version":2},"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/2405.15655/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":"2405.15655","created_at":"2026-07-05T09:06:05.860073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.15655v2","created_at":"2026-07-05T09:06:05.860073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15655","created_at":"2026-07-05T09:06:05.860073+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2ME4PET2AHC","created_at":"2026-07-05T09:06:05.860073+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2ME4PET2AHCOBRB","created_at":"2026-07-05T09:06:05.860073+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2ME4PET","created_at":"2026-07-05T09:06:05.860073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00561","citing_title":"FreeTalk:A plug-and-play and black-box defense against speech synthesis attacks","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ","json":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ.json","graph_json":"https://pith.science/api/pith-number/C2ME4PET2AHCOBRBPWCT2MPNZQ/graph.json","events_json":"https://pith.science/api/pith-number/C2ME4PET2AHCOBRBPWCT2MPNZQ/events.json","paper":"https://pith.science/paper/C2ME4PET"},"agent_actions":{"view_html":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ","download_json":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ.json","view_paper":"https://pith.science/paper/C2ME4PET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.15655&json=true","fetch_graph":"https://pith.science/api/pith-number/C2ME4PET2AHCOBRBPWCT2MPNZQ/graph.json","fetch_events":"https://pith.science/api/pith-number/C2ME4PET2AHCOBRBPWCT2MPNZQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ/action/storage_attestation","attest_author":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ/action/author_attestation","sign_citation":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ/action/citation_signature","submit_replication":"https://pith.science/pith/C2ME4PET2AHCOBRBPWCT2MPNZQ/action/replication_record"}},"created_at":"2026-07-05T09:06:05.860073+00:00","updated_at":"2026-07-05T09:06:05.860073+00:00"}