{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4WHSCLFDGOWUP6LBOHUF5EE4OL","short_pith_number":"pith:4WHSCLFD","schema_version":"1.0","canonical_sha256":"e58f212ca333ad47f96171e85e909c72e4181526949c90d5c2a5a3e9c7dc620b","source":{"kind":"arxiv","id":"2412.04510","version":2},"attestation_state":"computed","paper":{"title":"A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Alessandro Marchetto, Jinhan Kim, Masoud Jamshidiyan Tehrani, Paolo Tonella, Rosmael Zidane Lekeufack Foulefack","submitted_at":"2024-12-04T09:49:55Z","abstract_excerpt":"The advent of deep learning and its astonishing performance has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are susceptible to mispredictions when small, adversarial changes are introduced into their input. Such mis-predictions can be triggered in the real world and can result in a failure of the entire system. In recent years, a growing number of research works have investigated ways to mount attacks against autonomous vehicles that exploit deep learning components. Such attacks are directed toward elements of the environment wh"},"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":"2412.04510","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-04T09:49:55Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"0ea201c46a2de45f3eacfc9d7e27da3f3ec857b2610bbe3ccd7f85902bba8d75","abstract_canon_sha256":"4fa05e501824d40b70b7faf5dc909a79bae043bbfe068693bd62893cb27b9a92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:58.082374Z","signature_b64":"ZeFyi+idDp1uHXmmQA5kDdNCDw0P68j5E6qAKNrMPWT65bLs6rIVcDTKCcDTJQE0U6cvmpUu97awz4qkjiNdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e58f212ca333ad47f96171e85e909c72e4181526949c90d5c2a5a3e9c7dc620b","last_reissued_at":"2026-07-05T11:52:58.081881Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:58.081881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Alessandro Marchetto, Jinhan Kim, Masoud Jamshidiyan Tehrani, Paolo Tonella, Rosmael Zidane Lekeufack Foulefack","submitted_at":"2024-12-04T09:49:55Z","abstract_excerpt":"The advent of deep learning and its astonishing performance has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are susceptible to mispredictions when small, adversarial changes are introduced into their input. Such mis-predictions can be triggered in the real world and can result in a failure of the entire system. In recent years, a growing number of research works have investigated ways to mount attacks against autonomous vehicles that exploit deep learning components. Such attacks are directed toward elements of the environment wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04510","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/2412.04510/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":"2412.04510","created_at":"2026-07-05T11:52:58.081938+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.04510v2","created_at":"2026-07-05T11:52:58.081938+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04510","created_at":"2026-07-05T11:52:58.081938+00:00"},{"alias_kind":"pith_short_12","alias_value":"4WHSCLFDGOWU","created_at":"2026-07-05T11:52:58.081938+00:00"},{"alias_kind":"pith_short_16","alias_value":"4WHSCLFDGOWUP6LB","created_at":"2026-07-05T11:52:58.081938+00:00"},{"alias_kind":"pith_short_8","alias_value":"4WHSCLFD","created_at":"2026-07-05T11:52:58.081938+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/4WHSCLFDGOWUP6LBOHUF5EE4OL","json":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL.json","graph_json":"https://pith.science/api/pith-number/4WHSCLFDGOWUP6LBOHUF5EE4OL/graph.json","events_json":"https://pith.science/api/pith-number/4WHSCLFDGOWUP6LBOHUF5EE4OL/events.json","paper":"https://pith.science/paper/4WHSCLFD"},"agent_actions":{"view_html":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL","download_json":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL.json","view_paper":"https://pith.science/paper/4WHSCLFD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.04510&json=true","fetch_graph":"https://pith.science/api/pith-number/4WHSCLFDGOWUP6LBOHUF5EE4OL/graph.json","fetch_events":"https://pith.science/api/pith-number/4WHSCLFDGOWUP6LBOHUF5EE4OL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL/action/storage_attestation","attest_author":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL/action/author_attestation","sign_citation":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL/action/citation_signature","submit_replication":"https://pith.science/pith/4WHSCLFDGOWUP6LBOHUF5EE4OL/action/replication_record"}},"created_at":"2026-07-05T11:52:58.081938+00:00","updated_at":"2026-07-05T11:52:58.081938+00:00"}