{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RKACRBSRZ4MW7H2XXSGYUEC7NR","short_pith_number":"pith:RKACRBSR","schema_version":"1.0","canonical_sha256":"8a80288651cf196f9f57bc8d8a105f6c449d4f1f4ddecd089964e635fae45cbf","source":{"kind":"arxiv","id":"2505.11532","version":2},"attestation_state":"computed","paper":{"title":"Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.RO","authors_text":"Cheng Chen, Nafis S Munir, Xiangwei Zhou, Xugui Zhou, Yuhong Wang","submitted_at":"2025-05-14T02:05:34Z","abstract_excerpt":"Autonomous driving systems (ADS) increasingly rely on deep learning-based perception models, which remain vulnerable to adversarial attacks. In this paper, we revisit adversarial attacks and defense methods, focusing on road sign recognition and lead object detection and prediction (e.g., relative distance). Using a Level-2 production ADS, OpenPilot by Comma$.$ai, and the widely adopted YOLO model, we systematically examine the impact of adversarial perturbations and assess defense techniques, including adversarial training, image processing, contrastive learning, and diffusion models. Our exp"},"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":"2505.11532","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-14T02:05:34Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"a42a67c0662238369e9916d1e9fa2f1174b7b238725ad0a6ba230b6ccec339bf","abstract_canon_sha256":"545396f63bb2e96fa265b455b58c5f728b22039712815d676c9db70d71d4c4f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:20.681932Z","signature_b64":"0EIYcrq9p/+Z+TbwQvMFz/oahSHXbecBj/ee9nb8dIK56osFh95mbVk5u5NQ3BRRSngOrUnm2XLLDwifoVd6DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a80288651cf196f9f57bc8d8a105f6c449d4f1f4ddecd089964e635fae45cbf","last_reissued_at":"2026-07-05T11:08:20.681435Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:20.681435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.RO","authors_text":"Cheng Chen, Nafis S Munir, Xiangwei Zhou, Xugui Zhou, Yuhong Wang","submitted_at":"2025-05-14T02:05:34Z","abstract_excerpt":"Autonomous driving systems (ADS) increasingly rely on deep learning-based perception models, which remain vulnerable to adversarial attacks. In this paper, we revisit adversarial attacks and defense methods, focusing on road sign recognition and lead object detection and prediction (e.g., relative distance). Using a Level-2 production ADS, OpenPilot by Comma$.$ai, and the widely adopted YOLO model, we systematically examine the impact of adversarial perturbations and assess defense techniques, including adversarial training, image processing, contrastive learning, and diffusion models. Our exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11532","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/2505.11532/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":"2505.11532","created_at":"2026-07-05T11:08:20.681497+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.11532v2","created_at":"2026-07-05T11:08:20.681497+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11532","created_at":"2026-07-05T11:08:20.681497+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKACRBSRZ4MW","created_at":"2026-07-05T11:08:20.681497+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKACRBSRZ4MW7H2X","created_at":"2026-07-05T11:08:20.681497+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKACRBSR","created_at":"2026-07-05T11:08:20.681497+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/RKACRBSRZ4MW7H2XXSGYUEC7NR","json":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR.json","graph_json":"https://pith.science/api/pith-number/RKACRBSRZ4MW7H2XXSGYUEC7NR/graph.json","events_json":"https://pith.science/api/pith-number/RKACRBSRZ4MW7H2XXSGYUEC7NR/events.json","paper":"https://pith.science/paper/RKACRBSR"},"agent_actions":{"view_html":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR","download_json":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR.json","view_paper":"https://pith.science/paper/RKACRBSR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.11532&json=true","fetch_graph":"https://pith.science/api/pith-number/RKACRBSRZ4MW7H2XXSGYUEC7NR/graph.json","fetch_events":"https://pith.science/api/pith-number/RKACRBSRZ4MW7H2XXSGYUEC7NR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR/action/storage_attestation","attest_author":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR/action/author_attestation","sign_citation":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR/action/citation_signature","submit_replication":"https://pith.science/pith/RKACRBSRZ4MW7H2XXSGYUEC7NR/action/replication_record"}},"created_at":"2026-07-05T11:08:20.681497+00:00","updated_at":"2026-07-05T11:08:20.681497+00:00"}