{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Z366PVCDHL4VZ4OTD254QQ6O5N","short_pith_number":"pith:Z366PVCD","schema_version":"1.0","canonical_sha256":"cefde7d4433af95cf1d31ebbc843ceeb40d4a3cdfab1b153735598a9397b11e8","source":{"kind":"arxiv","id":"1901.03597","version":3},"attestation_state":"computed","paper":{"title":"CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Ilan Shelef, Tom Mahler, Yisroel Mirsky, Yuval Elovici","submitted_at":"2019-01-11T14:41:31Z","abstract_excerpt":"In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market.\n  In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack u"},"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":"1901.03597","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-01-11T14:41:31Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"95630fb94e38ec202e459a5f892291aa08959a7d353f27aa13554403e7cb6cfd","abstract_canon_sha256":"f32cd3df5d50184185cc907a6896499f0d9f57d07f6174ed25f8c60d6d82bf67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:02.541425Z","signature_b64":"CiWy3Ytf7iAuN9vT9vrCv/PmAVnioeKuqgGmnvFRrVW8T3fJjCGgOJKrHE4vfswOWWkQJRQUyyvbw9e8XGd3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cefde7d4433af95cf1d31ebbc843ceeb40d4a3cdfab1b153735598a9397b11e8","last_reissued_at":"2026-05-17T23:44:02.541027Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:02.541027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Ilan Shelef, Tom Mahler, Yisroel Mirsky, Yuval Elovici","submitted_at":"2019-01-11T14:41:31Z","abstract_excerpt":"In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market.\n  In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03597","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":""},"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":"1901.03597","created_at":"2026-05-17T23:44:02.541087+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03597v3","created_at":"2026-05-17T23:44:02.541087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03597","created_at":"2026-05-17T23:44:02.541087+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z366PVCDHL4V","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z366PVCDHL4VZ4OT","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z366PVCD","created_at":"2026-05-18T12:33:33.725879+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/Z366PVCDHL4VZ4OTD254QQ6O5N","json":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N.json","graph_json":"https://pith.science/api/pith-number/Z366PVCDHL4VZ4OTD254QQ6O5N/graph.json","events_json":"https://pith.science/api/pith-number/Z366PVCDHL4VZ4OTD254QQ6O5N/events.json","paper":"https://pith.science/paper/Z366PVCD"},"agent_actions":{"view_html":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N","download_json":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N.json","view_paper":"https://pith.science/paper/Z366PVCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03597&json=true","fetch_graph":"https://pith.science/api/pith-number/Z366PVCDHL4VZ4OTD254QQ6O5N/graph.json","fetch_events":"https://pith.science/api/pith-number/Z366PVCDHL4VZ4OTD254QQ6O5N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N/action/storage_attestation","attest_author":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N/action/author_attestation","sign_citation":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N/action/citation_signature","submit_replication":"https://pith.science/pith/Z366PVCDHL4VZ4OTD254QQ6O5N/action/replication_record"}},"created_at":"2026-05-17T23:44:02.541087+00:00","updated_at":"2026-05-17T23:44:02.541087+00:00"}