{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:53MGPZZZ2SMUSLAQHSQRYNWHW5","short_pith_number":"pith:53MGPZZZ","schema_version":"1.0","canonical_sha256":"eed867e739d499492c103ca11c36c7b775ed2ce6e302fe9f90ab5edd4c32c33a","source":{"kind":"arxiv","id":"2212.09067","version":1},"attestation_state":"computed","paper":{"title":"Fine-Tuning Is All You Need to Mitigate Backdoor Attacks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Mathias Humbert, Pascal Berrang, Xinlei He, Yang Zhang, Zeyang Sha","submitted_at":"2022-12-18T11:30:59Z","abstract_excerpt":"Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increasingly complex and often require high computational resources or may also jeopardize models' utility. In this work, we show that fine-tuning, one of the most common and easy-to-adopt machine learning training operations, can effectively remove backdoors from machine learning models while maintaining high model utility. Extensive experiments over three machine learning paradigms show that fine-tuning and our newly prop"},"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":"2212.09067","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-12-18T11:30:59Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"b66198dd38d343980bc558f560f4765ba5fb923bbe2aa377d83663a80877d814","abstract_canon_sha256":"48243d00798718d6933fe1b87be581746cff67b5f67b9e19540c50c42cdb7f95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:17.634025Z","signature_b64":"ZCJAlaC6Ur4FWGX+ziQlQIsJEfP6lXIBb+Q19JXdRq008ZKJcUE5DWP/TQ1dRZv+rNaV9py6NkejsEtGzYCfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eed867e739d499492c103ca11c36c7b775ed2ce6e302fe9f90ab5edd4c32c33a","last_reissued_at":"2026-07-05T05:26:17.633490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:17.633490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-Tuning Is All You Need to Mitigate Backdoor Attacks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Mathias Humbert, Pascal Berrang, Xinlei He, Yang Zhang, Zeyang Sha","submitted_at":"2022-12-18T11:30:59Z","abstract_excerpt":"Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increasingly complex and often require high computational resources or may also jeopardize models' utility. In this work, we show that fine-tuning, one of the most common and easy-to-adopt machine learning training operations, can effectively remove backdoors from machine learning models while maintaining high model utility. Extensive experiments over three machine learning paradigms show that fine-tuning and our newly prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09067","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/2212.09067/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":"2212.09067","created_at":"2026-07-05T05:26:17.633556+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09067v1","created_at":"2026-07-05T05:26:17.633556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09067","created_at":"2026-07-05T05:26:17.633556+00:00"},{"alias_kind":"pith_short_12","alias_value":"53MGPZZZ2SMU","created_at":"2026-07-05T05:26:17.633556+00:00"},{"alias_kind":"pith_short_16","alias_value":"53MGPZZZ2SMUSLAQ","created_at":"2026-07-05T05:26:17.633556+00:00"},{"alias_kind":"pith_short_8","alias_value":"53MGPZZZ","created_at":"2026-07-05T05:26:17.633556+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20254","citing_title":"Quantization as a Malicious Task: Removing Quantization-Conditioned Backdoors via Task Arithmetic","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03344","citing_title":"RogueMerge: Robust and Unified Attacks against LLM Model Merging","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2509.08089","citing_title":"Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08766","citing_title":"Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09688","citing_title":"Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5","json":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5.json","graph_json":"https://pith.science/api/pith-number/53MGPZZZ2SMUSLAQHSQRYNWHW5/graph.json","events_json":"https://pith.science/api/pith-number/53MGPZZZ2SMUSLAQHSQRYNWHW5/events.json","paper":"https://pith.science/paper/53MGPZZZ"},"agent_actions":{"view_html":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5","download_json":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5.json","view_paper":"https://pith.science/paper/53MGPZZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09067&json=true","fetch_graph":"https://pith.science/api/pith-number/53MGPZZZ2SMUSLAQHSQRYNWHW5/graph.json","fetch_events":"https://pith.science/api/pith-number/53MGPZZZ2SMUSLAQHSQRYNWHW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5/action/storage_attestation","attest_author":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5/action/author_attestation","sign_citation":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5/action/citation_signature","submit_replication":"https://pith.science/pith/53MGPZZZ2SMUSLAQHSQRYNWHW5/action/replication_record"}},"created_at":"2026-07-05T05:26:17.633556+00:00","updated_at":"2026-07-05T05:26:17.633556+00:00"}