{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OJHJENGR7YDBRGGWMTFBA4RU2K","short_pith_number":"pith:OJHJENGR","schema_version":"1.0","canonical_sha256":"724e9234d1fe061898d664ca107234d29a1fc535e574f11355281360e5ebea30","source":{"kind":"arxiv","id":"2607.27936","version":1},"attestation_state":"computed","paper":{"title":"Benign on Label, Malicious by Design: Clean-Label Dormant-to-Activated Backdoor via Machine Unlearning with Removable Camouflage","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Baogang Song, Can Li, Dongdong Zhao, Fan He, Qihang Ge, Xiang Yao","submitted_at":"2026-07-30T09:46:02Z","abstract_excerpt":"Existing backdoor attacks often become effective immediately after backdoor implantation and may therefore be exposed before exploitation. Machine unlearning activated dormant backdoors mitigate such behavioral exposure by remaining inactive after training and becoming effective only after selected training records are unlearned. However, existing methods struggle to simultaneously achieve a low pre-unlearning attack success rate and strong post-unlearning activation under clean-label constraints and realistic unlearning requests. Achieving this transition requires jointly establishing a persi"},"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":"2607.27936","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-07-30T09:46:02Z","cross_cats_sorted":[],"title_canon_sha256":"a69b05963b9ef928fbf3f9e1a5a7800078e2a87134064402174a3fd4ee4af858","abstract_canon_sha256":"831c8e040dc76059d0f012514442291dbfa3f4a619a1f62eeb56e7a4c4007e03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"724e9234d1fe061898d664ca107234d29a1fc535e574f11355281360e5ebea30","last_reissued_at":"2026-07-31T01:34:50.501840Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:34:50.501840Z"},"graph_snapshot":{"paper":{"title":"Benign on Label, Malicious by Design: Clean-Label Dormant-to-Activated Backdoor via Machine Unlearning with Removable Camouflage","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Baogang Song, Can Li, Dongdong Zhao, Fan He, Qihang Ge, Xiang Yao","submitted_at":"2026-07-30T09:46:02Z","abstract_excerpt":"Existing backdoor attacks often become effective immediately after backdoor implantation and may therefore be exposed before exploitation. Machine unlearning activated dormant backdoors mitigate such behavioral exposure by remaining inactive after training and becoming effective only after selected training records are unlearned. However, existing methods struggle to simultaneously achieve a low pre-unlearning attack success rate and strong post-unlearning activation under clean-label constraints and realistic unlearning requests. Achieving this transition requires jointly establishing a persi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27936","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/2607.27936/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":"2607.27936","created_at":"2026-07-31T01:34:50.504987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27936v1","created_at":"2026-07-31T01:34:50.504987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27936","created_at":"2026-07-31T01:34:50.504987+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJHJENGR7YDB","created_at":"2026-07-31T01:34:50.504987+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJHJENGR7YDBRGGW","created_at":"2026-07-31T01:34:50.504987+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJHJENGR","created_at":"2026-07-31T01:34:50.504987+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/OJHJENGR7YDBRGGWMTFBA4RU2K","json":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K.json","graph_json":"https://pith.science/api/pith-number/OJHJENGR7YDBRGGWMTFBA4RU2K/graph.json","events_json":"https://pith.science/api/pith-number/OJHJENGR7YDBRGGWMTFBA4RU2K/events.json","paper":"https://pith.science/paper/OJHJENGR"},"agent_actions":{"view_html":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K","download_json":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K.json","view_paper":"https://pith.science/paper/OJHJENGR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27936&json=true","fetch_graph":"https://pith.science/api/pith-number/OJHJENGR7YDBRGGWMTFBA4RU2K/graph.json","fetch_events":"https://pith.science/api/pith-number/OJHJENGR7YDBRGGWMTFBA4RU2K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K/action/storage_attestation","attest_author":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K/action/author_attestation","sign_citation":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K/action/citation_signature","submit_replication":"https://pith.science/pith/OJHJENGR7YDBRGGWMTFBA4RU2K/action/replication_record"}},"created_at":"2026-07-31T01:34:50.504987+00:00","updated_at":"2026-07-31T01:34:50.504987+00:00"}