{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4YPJANWL6SGRJ27OCITG2WISDA","short_pith_number":"pith:4YPJANWL","schema_version":"1.0","canonical_sha256":"e61e9036cbf48d14ebee12266d59121835299ec507a510a47c10beb4138226c5","source":{"kind":"arxiv","id":"1912.00461","version":2},"attestation_state":"computed","paper":{"title":"AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdullah Hamdi, Ali Thabet, Bernard Ghanem, Sara Rojas","submitted_at":"2019-12-01T18:13:23Z","abstract_excerpt":"Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against via simple statistical methods. To this extent, we de"},"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":"1912.00461","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-12-01T18:13:23Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"7535ac73b03b3b8d1bfbbe2e190ecf1c41b8995cddb74d078c7dcba2c90a03cf","abstract_canon_sha256":"98100b78aeb9d9389429b7f0f2c9a4697f61f3683e8e8ae4e0d87659619f58fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:54:41.915740Z","signature_b64":"60k4vVajSz2F0UXGcVJoL32d99cWWoOGmXcyNwKU0botFavK0TlQx1uFvctMUrBZ8JOvjQk7pWxYbWEjIod/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e61e9036cbf48d14ebee12266d59121835299ec507a510a47c10beb4138226c5","last_reissued_at":"2026-07-05T01:54:41.915263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:54:41.915263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdullah Hamdi, Ali Thabet, Bernard Ghanem, Sara Rojas","submitted_at":"2019-12-01T18:13:23Z","abstract_excerpt":"Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against via simple statistical methods. To this extent, we de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.00461","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/1912.00461/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":"1912.00461","created_at":"2026-07-05T01:54:41.915328+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.00461v2","created_at":"2026-07-05T01:54:41.915328+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.00461","created_at":"2026-07-05T01:54:41.915328+00:00"},{"alias_kind":"pith_short_12","alias_value":"4YPJANWL6SGR","created_at":"2026-07-05T01:54:41.915328+00:00"},{"alias_kind":"pith_short_16","alias_value":"4YPJANWL6SGRJ27O","created_at":"2026-07-05T01:54:41.915328+00:00"},{"alias_kind":"pith_short_8","alias_value":"4YPJANWL","created_at":"2026-07-05T01:54:41.915328+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.21163","citing_title":"Generating Adversarial Point Clouds Using Diffusion Model","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA","json":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA.json","graph_json":"https://pith.science/api/pith-number/4YPJANWL6SGRJ27OCITG2WISDA/graph.json","events_json":"https://pith.science/api/pith-number/4YPJANWL6SGRJ27OCITG2WISDA/events.json","paper":"https://pith.science/paper/4YPJANWL"},"agent_actions":{"view_html":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA","download_json":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA.json","view_paper":"https://pith.science/paper/4YPJANWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.00461&json=true","fetch_graph":"https://pith.science/api/pith-number/4YPJANWL6SGRJ27OCITG2WISDA/graph.json","fetch_events":"https://pith.science/api/pith-number/4YPJANWL6SGRJ27OCITG2WISDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA/action/storage_attestation","attest_author":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA/action/author_attestation","sign_citation":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA/action/citation_signature","submit_replication":"https://pith.science/pith/4YPJANWL6SGRJ27OCITG2WISDA/action/replication_record"}},"created_at":"2026-07-05T01:54:41.915328+00:00","updated_at":"2026-07-05T01:54:41.915328+00:00"}