{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OKZGMV6UG35KIUEM7X2Y7JLFJ6","short_pith_number":"pith:OKZGMV6U","schema_version":"1.0","canonical_sha256":"72b26657d436faa4508cfdf58fa5654f80e1c262a0e50a7610ced05ba030c0af","source":{"kind":"arxiv","id":"2003.01908","version":2},"attestation_state":"computed","paper":{"title":"Denoised Smoothing: A Provable Defense for Pretrained Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashish Kapoor, Greg Yang, Hadi Salman, J. Zico Kolter, Mingjie Sun","submitted_at":"2020-03-04T06:15:55Z","abstract_excerpt":"We present a method for provably defending any pretrained image classifier against $\\ell_p$ adversarial attacks. This method, for instance, allows public vision API providers and users to seamlessly convert pretrained non-robust classification services into provably robust ones. By prepending a custom-trained denoiser to any off-the-shelf image classifier and using randomized smoothing, we effectively create a new classifier that is guaranteed to be $\\ell_p$-robust to adversarial examples, without modifying the pretrained classifier. Our approach applies to both the white-box and the black-box"},"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":"2003.01908","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-04T06:15:55Z","cross_cats_sorted":["cs.CR","cs.CV","stat.ML"],"title_canon_sha256":"4f0028ed7447cdd1ff7a5026e5273013f685db964dc915846cd89ae0c9c6006b","abstract_canon_sha256":"bf3436ef1dfaddf15ac94f0db941a8f6f20aeab6ba8633ee2250a841b55637f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:36:28.834067Z","signature_b64":"ECownMXKTug1BSM3+VZ/Qy/QRIqfbO62naav3KHOf7y6DiKcr8ynolnTiyln9lYW+wb8zNcDSutQ2nOQJXo4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72b26657d436faa4508cfdf58fa5654f80e1c262a0e50a7610ced05ba030c0af","last_reissued_at":"2026-07-05T01:36:28.833548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:36:28.833548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Denoised Smoothing: A Provable Defense for Pretrained Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashish Kapoor, Greg Yang, Hadi Salman, J. Zico Kolter, Mingjie Sun","submitted_at":"2020-03-04T06:15:55Z","abstract_excerpt":"We present a method for provably defending any pretrained image classifier against $\\ell_p$ adversarial attacks. This method, for instance, allows public vision API providers and users to seamlessly convert pretrained non-robust classification services into provably robust ones. By prepending a custom-trained denoiser to any off-the-shelf image classifier and using randomized smoothing, we effectively create a new classifier that is guaranteed to be $\\ell_p$-robust to adversarial examples, without modifying the pretrained classifier. Our approach applies to both the white-box and the black-box"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.01908","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/2003.01908/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":"2003.01908","created_at":"2026-07-05T01:36:28.833610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.01908v2","created_at":"2026-07-05T01:36:28.833610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.01908","created_at":"2026-07-05T01:36:28.833610+00:00"},{"alias_kind":"pith_short_12","alias_value":"OKZGMV6UG35K","created_at":"2026-07-05T01:36:28.833610+00:00"},{"alias_kind":"pith_short_16","alias_value":"OKZGMV6UG35KIUEM","created_at":"2026-07-05T01:36:28.833610+00:00"},{"alias_kind":"pith_short_8","alias_value":"OKZGMV6U","created_at":"2026-07-05T01:36:28.833610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07690","citing_title":"Fortifying Time Series: DTW-Certified Robust Anomaly Detection","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6","json":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6.json","graph_json":"https://pith.science/api/pith-number/OKZGMV6UG35KIUEM7X2Y7JLFJ6/graph.json","events_json":"https://pith.science/api/pith-number/OKZGMV6UG35KIUEM7X2Y7JLFJ6/events.json","paper":"https://pith.science/paper/OKZGMV6U"},"agent_actions":{"view_html":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6","download_json":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6.json","view_paper":"https://pith.science/paper/OKZGMV6U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.01908&json=true","fetch_graph":"https://pith.science/api/pith-number/OKZGMV6UG35KIUEM7X2Y7JLFJ6/graph.json","fetch_events":"https://pith.science/api/pith-number/OKZGMV6UG35KIUEM7X2Y7JLFJ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6/action/storage_attestation","attest_author":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6/action/author_attestation","sign_citation":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6/action/citation_signature","submit_replication":"https://pith.science/pith/OKZGMV6UG35KIUEM7X2Y7JLFJ6/action/replication_record"}},"created_at":"2026-07-05T01:36:28.833610+00:00","updated_at":"2026-07-05T01:36:28.833610+00:00"}