{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JHATWWETHAV73IN3ICZYRXKAPH","short_pith_number":"pith:JHATWWET","schema_version":"1.0","canonical_sha256":"49c13b5893382bfda1bb40b388dd4079df61527967ad60a7c71c24e58702c6a2","source":{"kind":"arxiv","id":"2407.10052","version":2},"attestation_state":"computed","paper":{"title":"Augmented Neural Fine-Tuning for Efficient Backdoor Purification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abdullah Al Arafat, Nazanin Rahnavard, Nazmul Karim, Umar Khalid, Zhishan Guo","submitted_at":"2024-07-14T02:36:54Z","abstract_excerpt":"Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain types of triggers or poisoning mechanisms. State-of-the-art (SOTA) defenses employ too-sophisticated mechanisms that require either a computationally expensive adversarial search module for reverse-engineering the trigger distribution or an over-sensitive hyper-parameter selection module. Moreover, they offer sub-par performance in challenging scenarios, e.g., limited validation data and strong attacks. In this paper, we p"},"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":"2407.10052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-14T02:36:54Z","cross_cats_sorted":[],"title_canon_sha256":"ef32b3000ce2fec5a68ae3b0778b167746ece0e110f85144c4ef9181c4285300","abstract_canon_sha256":"fa5e6d2c85044391f631eb8ba929b64f3f489ec22a433f4b8bb045f5e24dc5f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:57.087012Z","signature_b64":"eUL6y64AiYzgeq9PLDT1CvgFSNpcnCIhyjz5E0o675ApkAWxQB1zM9tJehG0ipYaqYgO659C6XszyyOJILqIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49c13b5893382bfda1bb40b388dd4079df61527967ad60a7c71c24e58702c6a2","last_reissued_at":"2026-07-05T08:44:57.086505Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:57.086505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Augmented Neural Fine-Tuning for Efficient Backdoor Purification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abdullah Al Arafat, Nazanin Rahnavard, Nazmul Karim, Umar Khalid, Zhishan Guo","submitted_at":"2024-07-14T02:36:54Z","abstract_excerpt":"Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain types of triggers or poisoning mechanisms. State-of-the-art (SOTA) defenses employ too-sophisticated mechanisms that require either a computationally expensive adversarial search module for reverse-engineering the trigger distribution or an over-sensitive hyper-parameter selection module. Moreover, they offer sub-par performance in challenging scenarios, e.g., limited validation data and strong attacks. In this paper, we p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10052","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/2407.10052/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":"2407.10052","created_at":"2026-07-05T08:44:57.086570+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.10052v2","created_at":"2026-07-05T08:44:57.086570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10052","created_at":"2026-07-05T08:44:57.086570+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHATWWETHAV7","created_at":"2026-07-05T08:44:57.086570+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHATWWETHAV73IN3","created_at":"2026-07-05T08:44:57.086570+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHATWWET","created_at":"2026-07-05T08:44:57.086570+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/JHATWWETHAV73IN3ICZYRXKAPH","json":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH.json","graph_json":"https://pith.science/api/pith-number/JHATWWETHAV73IN3ICZYRXKAPH/graph.json","events_json":"https://pith.science/api/pith-number/JHATWWETHAV73IN3ICZYRXKAPH/events.json","paper":"https://pith.science/paper/JHATWWET"},"agent_actions":{"view_html":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH","download_json":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH.json","view_paper":"https://pith.science/paper/JHATWWET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.10052&json=true","fetch_graph":"https://pith.science/api/pith-number/JHATWWETHAV73IN3ICZYRXKAPH/graph.json","fetch_events":"https://pith.science/api/pith-number/JHATWWETHAV73IN3ICZYRXKAPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH/action/storage_attestation","attest_author":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH/action/author_attestation","sign_citation":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH/action/citation_signature","submit_replication":"https://pith.science/pith/JHATWWETHAV73IN3ICZYRXKAPH/action/replication_record"}},"created_at":"2026-07-05T08:44:57.086570+00:00","updated_at":"2026-07-05T08:44:57.086570+00:00"}