{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6Z4RUD25UELKNSHHORL62K7WDX","short_pith_number":"pith:6Z4RUD25","schema_version":"1.0","canonical_sha256":"f6791a0f5da116a6c8e77457ed2bf61dfdd064ff98a98cff6441bdf19df66698","source":{"kind":"arxiv","id":"2202.10276","version":2},"attestation_state":"computed","paper":{"title":"Poisoning Attacks and Defenses on Artificial Intelligence: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chan Yeob Yeun, Chung-Suk Cho, Ernesto Damiani, Hussam Al Hamadi, Miguel A. Ramirez, Song-Kyoo Kim, Tae-Yeon Kim, Young-Ji Byon","submitted_at":"2022-02-21T14:43:38Z","abstract_excerpt":"Machine learning models have been widely adopted in several fields. However, most recent studies have shown several vulnerabilities from attacks with a potential to jeopardize the integrity of the model, presenting a new window of research opportunity in terms of cyber-security. This survey is conducted with a main intention of highlighting the most relevant information related to security vulnerabilities in the context of machine learning (ML) classifiers; more specifically, directed towards training procedures against data poisoning attacks, representing a type of attack that consists of tam"},"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":"2202.10276","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-02-21T14:43:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"86680b57dfbe259b62f11b9adaeda012d9b4f64971d3e65e913797e79ed18932","abstract_canon_sha256":"70f3db031d94cbb406f80189ee0d92240d65adcfa75713cabfe7eb4a4c9f19d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:59:04.523157Z","signature_b64":"OpxjqFaoDzE7Aehh3p7+TSNrdMUSsWFyPdha7u28cNcqRbJZGRk6Uge26kVU+GRYlD6s6Yil5gH4li2qdNteDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6791a0f5da116a6c8e77457ed2bf61dfdd064ff98a98cff6441bdf19df66698","last_reissued_at":"2026-07-05T03:59:04.522675Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:59:04.522675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Poisoning Attacks and Defenses on Artificial Intelligence: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chan Yeob Yeun, Chung-Suk Cho, Ernesto Damiani, Hussam Al Hamadi, Miguel A. Ramirez, Song-Kyoo Kim, Tae-Yeon Kim, Young-Ji Byon","submitted_at":"2022-02-21T14:43:38Z","abstract_excerpt":"Machine learning models have been widely adopted in several fields. However, most recent studies have shown several vulnerabilities from attacks with a potential to jeopardize the integrity of the model, presenting a new window of research opportunity in terms of cyber-security. This survey is conducted with a main intention of highlighting the most relevant information related to security vulnerabilities in the context of machine learning (ML) classifiers; more specifically, directed towards training procedures against data poisoning attacks, representing a type of attack that consists of tam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.10276","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/2202.10276/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":"2202.10276","created_at":"2026-07-05T03:59:04.522733+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.10276v2","created_at":"2026-07-05T03:59:04.522733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.10276","created_at":"2026-07-05T03:59:04.522733+00:00"},{"alias_kind":"pith_short_12","alias_value":"6Z4RUD25UELK","created_at":"2026-07-05T03:59:04.522733+00:00"},{"alias_kind":"pith_short_16","alias_value":"6Z4RUD25UELKNSHH","created_at":"2026-07-05T03:59:04.522733+00:00"},{"alias_kind":"pith_short_8","alias_value":"6Z4RUD25","created_at":"2026-07-05T03:59:04.522733+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2404.02696","citing_title":"Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition","ref_index":179,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX","json":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX.json","graph_json":"https://pith.science/api/pith-number/6Z4RUD25UELKNSHHORL62K7WDX/graph.json","events_json":"https://pith.science/api/pith-number/6Z4RUD25UELKNSHHORL62K7WDX/events.json","paper":"https://pith.science/paper/6Z4RUD25"},"agent_actions":{"view_html":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX","download_json":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX.json","view_paper":"https://pith.science/paper/6Z4RUD25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.10276&json=true","fetch_graph":"https://pith.science/api/pith-number/6Z4RUD25UELKNSHHORL62K7WDX/graph.json","fetch_events":"https://pith.science/api/pith-number/6Z4RUD25UELKNSHHORL62K7WDX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX/action/storage_attestation","attest_author":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX/action/author_attestation","sign_citation":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX/action/citation_signature","submit_replication":"https://pith.science/pith/6Z4RUD25UELKNSHHORL62K7WDX/action/replication_record"}},"created_at":"2026-07-05T03:59:04.522733+00:00","updated_at":"2026-07-05T03:59:04.522733+00:00"}