{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AT336BJUJ7HZVGAZCJLSCOPFRY","short_pith_number":"pith:AT336BJU","schema_version":"1.0","canonical_sha256":"04f7bf05344fcf9a981912572139e58e10c0a158fe2373cac4d84bbbcf937faf","source":{"kind":"arxiv","id":"2406.05810","version":1},"attestation_state":"computed","paper":{"title":"ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Shen, Chen Ma, Ningfei Wang, Qi Alfred Chen, Qian Wang, Zhengyu Zhao","submitted_at":"2024-06-09T14:53:50Z","abstract_excerpt":"Recent research in adversarial machine learning has focused on visual perception in Autonomous Driving (AD) and has shown that printed adversarial patches can attack object detectors. However, it is important to note that AD visual perception encompasses more than just object detection; it also includes Multiple Object Tracking (MOT). MOT enhances the robustness by compensating for object detection errors and requiring consistent object detection results across multiple frames before influencing tracking results and driving decisions. Thus, MOT makes attacks on object detection alone less effe"},"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":"2406.05810","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-09T14:53:50Z","cross_cats_sorted":[],"title_canon_sha256":"a2b6f4b817695e0fe92d4c3d63cb1077abaf195763ac3a6c8534153a31069467","abstract_canon_sha256":"dcbb5b8503515b2791edaa473185aa0d6d1f98b514a173d697e36c1462156d9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:29:33.843852Z","signature_b64":"O2DSBi+FeVIgcXiDX0KYuitHYe9e7RmX3P4JVORpfbQx4KnxvYbR7uOdfhM65bJyqkXuXl5Rzv988CaI5eCCBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04f7bf05344fcf9a981912572139e58e10c0a158fe2373cac4d84bbbcf937faf","last_reissued_at":"2026-07-05T08:29:33.843385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:29:33.843385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Shen, Chen Ma, Ningfei Wang, Qi Alfred Chen, Qian Wang, Zhengyu Zhao","submitted_at":"2024-06-09T14:53:50Z","abstract_excerpt":"Recent research in adversarial machine learning has focused on visual perception in Autonomous Driving (AD) and has shown that printed adversarial patches can attack object detectors. However, it is important to note that AD visual perception encompasses more than just object detection; it also includes Multiple Object Tracking (MOT). MOT enhances the robustness by compensating for object detection errors and requiring consistent object detection results across multiple frames before influencing tracking results and driving decisions. Thus, MOT makes attacks on object detection alone less effe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05810","kind":"arxiv","version":1},"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/2406.05810/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":"2406.05810","created_at":"2026-07-05T08:29:33.843443+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05810v1","created_at":"2026-07-05T08:29:33.843443+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05810","created_at":"2026-07-05T08:29:33.843443+00:00"},{"alias_kind":"pith_short_12","alias_value":"AT336BJUJ7HZ","created_at":"2026-07-05T08:29:33.843443+00:00"},{"alias_kind":"pith_short_16","alias_value":"AT336BJUJ7HZVGAZ","created_at":"2026-07-05T08:29:33.843443+00:00"},{"alias_kind":"pith_short_8","alias_value":"AT336BJU","created_at":"2026-07-05T08:29:33.843443+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02900","citing_title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","ref_index":249,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01301","citing_title":"From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY","json":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY.json","graph_json":"https://pith.science/api/pith-number/AT336BJUJ7HZVGAZCJLSCOPFRY/graph.json","events_json":"https://pith.science/api/pith-number/AT336BJUJ7HZVGAZCJLSCOPFRY/events.json","paper":"https://pith.science/paper/AT336BJU"},"agent_actions":{"view_html":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY","download_json":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY.json","view_paper":"https://pith.science/paper/AT336BJU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05810&json=true","fetch_graph":"https://pith.science/api/pith-number/AT336BJUJ7HZVGAZCJLSCOPFRY/graph.json","fetch_events":"https://pith.science/api/pith-number/AT336BJUJ7HZVGAZCJLSCOPFRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY/action/storage_attestation","attest_author":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY/action/author_attestation","sign_citation":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY/action/citation_signature","submit_replication":"https://pith.science/pith/AT336BJUJ7HZVGAZCJLSCOPFRY/action/replication_record"}},"created_at":"2026-07-05T08:29:33.843443+00:00","updated_at":"2026-07-05T08:29:33.843443+00:00"}