{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:MWEI7OGACE7X6JXGV7HSM3BY6T","short_pith_number":"pith:MWEI7OGA","schema_version":"1.0","canonical_sha256":"65888fb8c0113f7f26e6afcf266c38f4f7067906645ac52d3611fefc5a2cb1a2","source":{"kind":"arxiv","id":"2202.07261","version":4},"attestation_state":"computed","paper":{"title":"Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Daizong Liu, Qianjiang Hu, Wei Hu","submitted_at":"2022-02-15T09:16:12Z","abstract_excerpt":"With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective -- Graph Spectral Domain Attack (GSDA), aiming to perturb "},"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":"2202.07261","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T09:16:12Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"f47e473fad1d72c6390e4482f1c1f4750eb4fa4916ea38e6b6da8f494d2ea54c","abstract_canon_sha256":"027c096c617d67051c38f8716ac598f16b24e9be12018a281a7352619ae58b01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:28:06.308156Z","signature_b64":"JJ2ZN8ysgbA2J+f6M214uIu4M8SNzwS0PTfSITFZQy0rluOJYrC9NGrbNsATdc0LURYYm76VX+iIemwsrsVxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65888fb8c0113f7f26e6afcf266c38f4f7067906645ac52d3611fefc5a2cb1a2","last_reissued_at":"2026-07-05T05:28:06.307772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:28:06.307772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Daizong Liu, Qianjiang Hu, Wei Hu","submitted_at":"2022-02-15T09:16:12Z","abstract_excerpt":"With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective -- Graph Spectral Domain Attack (GSDA), aiming to perturb "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07261","kind":"arxiv","version":4},"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/2202.07261/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":"2202.07261","created_at":"2026-07-05T05:28:06.307835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.07261v4","created_at":"2026-07-05T05:28:06.307835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07261","created_at":"2026-07-05T05:28:06.307835+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWEI7OGACE7X","created_at":"2026-07-05T05:28:06.307835+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWEI7OGACE7X6JXG","created_at":"2026-07-05T05:28:06.307835+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWEI7OGA","created_at":"2026-07-05T05:28:06.307835+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2412.00404","citing_title":"Hard-Label Black-Box Attacks on 3D Point Clouds","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T","json":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T.json","graph_json":"https://pith.science/api/pith-number/MWEI7OGACE7X6JXGV7HSM3BY6T/graph.json","events_json":"https://pith.science/api/pith-number/MWEI7OGACE7X6JXGV7HSM3BY6T/events.json","paper":"https://pith.science/paper/MWEI7OGA"},"agent_actions":{"view_html":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T","download_json":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T.json","view_paper":"https://pith.science/paper/MWEI7OGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.07261&json=true","fetch_graph":"https://pith.science/api/pith-number/MWEI7OGACE7X6JXGV7HSM3BY6T/graph.json","fetch_events":"https://pith.science/api/pith-number/MWEI7OGACE7X6JXGV7HSM3BY6T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T/action/storage_attestation","attest_author":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T/action/author_attestation","sign_citation":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T/action/citation_signature","submit_replication":"https://pith.science/pith/MWEI7OGACE7X6JXGV7HSM3BY6T/action/replication_record"}},"created_at":"2026-07-05T05:28:06.307835+00:00","updated_at":"2026-07-05T05:28:06.307835+00:00"}