{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5AT52NH3DX3STHJU77VX3FUBIK","short_pith_number":"pith:5AT52NH3","schema_version":"1.0","canonical_sha256":"e827dd34fb1df7299d34ffeb7d9681429f1ca978d2d6d9e57b49b6fb60f2931f","source":{"kind":"arxiv","id":"2401.02565","version":3},"attestation_state":"computed","paper":{"title":"Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","q-bio.TO"],"primary_cat":"eess.IV","authors_text":"Jacob M. Luber, Jai Prakash Veerla, Mohammad S. Nasr, Partha Sai Guttikonda, Poojitha Thota, Shirin Nilizadeh","submitted_at":"2024-01-04T22:49:15Z","abstract_excerpt":"In the context of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks. Leveraging the Kather Colon dataset with 7,180 H&E images across nine tissue types, our investigation employs Projected Gradient Descent (PGD) adversarial perturbation attacks to induce misclassifications intentionally. The outcomes reveal a 100% success rate in manipulating PLIP's predictions, underscoring its susceptibility to adversarial perturbations. The qualitative analysis of adver"},"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":"2401.02565","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-01-04T22:49:15Z","cross_cats_sorted":["cs.CV","q-bio.TO"],"title_canon_sha256":"b395fdc42c58bede79b5d2315f550d568bc8e05f6af480a2337154286244878b","abstract_canon_sha256":"d270bc97341f179a07fc19b820021307fd7d022d30a56baae531bcb9b635ffdc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:45.863151Z","signature_b64":"vuOKdws9bkFWLBUE82s84771CsGVfkM2Kb1UNBE/LBUT7AEk4PMQrn+AK+lH6jXV/Cg0ZLpulNyxlBHCtgBLCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e827dd34fb1df7299d34ffeb7d9681429f1ca978d2d6d9e57b49b6fb60f2931f","last_reissued_at":"2026-07-05T08:16:45.862685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:45.862685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","q-bio.TO"],"primary_cat":"eess.IV","authors_text":"Jacob M. Luber, Jai Prakash Veerla, Mohammad S. Nasr, Partha Sai Guttikonda, Poojitha Thota, Shirin Nilizadeh","submitted_at":"2024-01-04T22:49:15Z","abstract_excerpt":"In the context of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks. Leveraging the Kather Colon dataset with 7,180 H&E images across nine tissue types, our investigation employs Projected Gradient Descent (PGD) adversarial perturbation attacks to induce misclassifications intentionally. The outcomes reveal a 100% success rate in manipulating PLIP's predictions, underscoring its susceptibility to adversarial perturbations. The qualitative analysis of adver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02565","kind":"arxiv","version":3},"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/2401.02565/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":"2401.02565","created_at":"2026-07-05T08:16:45.862737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.02565v3","created_at":"2026-07-05T08:16:45.862737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02565","created_at":"2026-07-05T08:16:45.862737+00:00"},{"alias_kind":"pith_short_12","alias_value":"5AT52NH3DX3S","created_at":"2026-07-05T08:16:45.862737+00:00"},{"alias_kind":"pith_short_16","alias_value":"5AT52NH3DX3STHJU","created_at":"2026-07-05T08:16:45.862737+00:00"},{"alias_kind":"pith_short_8","alias_value":"5AT52NH3","created_at":"2026-07-05T08:16:45.862737+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/5AT52NH3DX3STHJU77VX3FUBIK","json":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK.json","graph_json":"https://pith.science/api/pith-number/5AT52NH3DX3STHJU77VX3FUBIK/graph.json","events_json":"https://pith.science/api/pith-number/5AT52NH3DX3STHJU77VX3FUBIK/events.json","paper":"https://pith.science/paper/5AT52NH3"},"agent_actions":{"view_html":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK","download_json":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK.json","view_paper":"https://pith.science/paper/5AT52NH3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.02565&json=true","fetch_graph":"https://pith.science/api/pith-number/5AT52NH3DX3STHJU77VX3FUBIK/graph.json","fetch_events":"https://pith.science/api/pith-number/5AT52NH3DX3STHJU77VX3FUBIK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK/action/storage_attestation","attest_author":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK/action/author_attestation","sign_citation":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK/action/citation_signature","submit_replication":"https://pith.science/pith/5AT52NH3DX3STHJU77VX3FUBIK/action/replication_record"}},"created_at":"2026-07-05T08:16:45.862737+00:00","updated_at":"2026-07-05T08:16:45.862737+00:00"}