{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SBHI6DGGXHQX2MSWTBGKA3FW77","short_pith_number":"pith:SBHI6DGG","schema_version":"1.0","canonical_sha256":"904e8f0cc6b9e17d3256984ca06cb6ffc0b61e260a437312101e4f5a4ed25c0e","source":{"kind":"arxiv","id":"2501.12269","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.SE","authors_text":"Andrea Stocco, Hannes Leonhard, Stefano Carlo Lambertenghi","submitted_at":"2025-01-21T16:40:44Z","abstract_excerpt":"Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures.\n  This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and gen"},"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":"2501.12269","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2025-01-21T16:40:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"304bb23802ee9121dab09d091143fbdcbf39dd8b474b50b4956f0ce32a9ea0d0","abstract_canon_sha256":"2aece83a5e9e00701a83b9338937282e05a98e60acbd94453f13ba8fa99d449d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:33.868295Z","signature_b64":"eQRdv7aQBYFu10iTsqoHrtDdNoKJEjYTkWzWJDu9ka/pGdPMKszXt8anHB9KL2XFRbq7f0y5ek8kxbgCpuJLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"904e8f0cc6b9e17d3256984ca06cb6ffc0b61e260a437312101e4f5a4ed25c0e","last_reissued_at":"2026-07-05T10:03:33.867847Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:33.867847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.SE","authors_text":"Andrea Stocco, Hannes Leonhard, Stefano Carlo Lambertenghi","submitted_at":"2025-01-21T16:40:44Z","abstract_excerpt":"Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures.\n  This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12269","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/2501.12269/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":"2501.12269","created_at":"2026-07-05T10:03:33.867921+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12269v1","created_at":"2026-07-05T10:03:33.867921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12269","created_at":"2026-07-05T10:03:33.867921+00:00"},{"alias_kind":"pith_short_12","alias_value":"SBHI6DGGXHQX","created_at":"2026-07-05T10:03:33.867921+00:00"},{"alias_kind":"pith_short_16","alias_value":"SBHI6DGGXHQX2MSW","created_at":"2026-07-05T10:03:33.867921+00:00"},{"alias_kind":"pith_short_8","alias_value":"SBHI6DGG","created_at":"2026-07-05T10:03:33.867921+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/SBHI6DGGXHQX2MSWTBGKA3FW77","json":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77.json","graph_json":"https://pith.science/api/pith-number/SBHI6DGGXHQX2MSWTBGKA3FW77/graph.json","events_json":"https://pith.science/api/pith-number/SBHI6DGGXHQX2MSWTBGKA3FW77/events.json","paper":"https://pith.science/paper/SBHI6DGG"},"agent_actions":{"view_html":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77","download_json":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77.json","view_paper":"https://pith.science/paper/SBHI6DGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12269&json=true","fetch_graph":"https://pith.science/api/pith-number/SBHI6DGGXHQX2MSWTBGKA3FW77/graph.json","fetch_events":"https://pith.science/api/pith-number/SBHI6DGGXHQX2MSWTBGKA3FW77/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77/action/storage_attestation","attest_author":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77/action/author_attestation","sign_citation":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77/action/citation_signature","submit_replication":"https://pith.science/pith/SBHI6DGGXHQX2MSWTBGKA3FW77/action/replication_record"}},"created_at":"2026-07-05T10:03:33.867921+00:00","updated_at":"2026-07-05T10:03:33.867921+00:00"}