{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:4BMQMU3UDPLIDQVWUQKCE5TDNC","short_pith_number":"pith:4BMQMU3U","schema_version":"1.0","canonical_sha256":"e0590653741bd681c2b6a414227663688e079a9ad0ee2e62b85b0e31c5299d96","source":{"kind":"arxiv","id":"2105.04839","version":1},"attestation_state":"computed","paper":{"title":"Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guiyu Tian, Wei Liu, Wenhao Jiang, Yadong Mu","submitted_at":"2021-05-11T07:48:39Z","abstract_excerpt":"This paper presents Poisoning MorphNet, the first backdoor attack method on point clouds. Conventional adversarial attack takes place in the inference stage, often fooling a model by perturbing samples. In contrast, backdoor attack aims to implant triggers into a model during the training stage, such that the victim model acts normally on the clean data unless a trigger is present in a sample. This work follows a typical setting of clean-label backdoor attack, where a few poisoned samples (with their content tampered yet labels unchanged) are injected into the training set. The unique contribu"},"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":"2105.04839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-05-11T07:48:39Z","cross_cats_sorted":[],"title_canon_sha256":"cf4e3d1f8b59abb638ede65e6e1908a85391ed19b211d464dc32de9f6b6ae256","abstract_canon_sha256":"b3923a6d6eec4ba588f7bf4a095548301e6b4387d3c7bbb7f870b1bcc775ef3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:39:24.915049Z","signature_b64":"TbeLt9t/PTAhy/LLadBP2EZNJEIm3ygmKFsOWjfqrlxFyak7NhKtpqYt1sM8P/YsVGK/a3u+byBGds/9esLoDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0590653741bd681c2b6a414227663688e079a9ad0ee2e62b85b0e31c5299d96","last_reissued_at":"2026-07-05T02:39:24.914578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:39:24.914578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guiyu Tian, Wei Liu, Wenhao Jiang, Yadong Mu","submitted_at":"2021-05-11T07:48:39Z","abstract_excerpt":"This paper presents Poisoning MorphNet, the first backdoor attack method on point clouds. Conventional adversarial attack takes place in the inference stage, often fooling a model by perturbing samples. In contrast, backdoor attack aims to implant triggers into a model during the training stage, such that the victim model acts normally on the clean data unless a trigger is present in a sample. This work follows a typical setting of clean-label backdoor attack, where a few poisoned samples (with their content tampered yet labels unchanged) are injected into the training set. The unique contribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.04839","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/2105.04839/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":"2105.04839","created_at":"2026-07-05T02:39:24.914637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.04839v1","created_at":"2026-07-05T02:39:24.914637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.04839","created_at":"2026-07-05T02:39:24.914637+00:00"},{"alias_kind":"pith_short_12","alias_value":"4BMQMU3UDPLI","created_at":"2026-07-05T02:39:24.914637+00:00"},{"alias_kind":"pith_short_16","alias_value":"4BMQMU3UDPLIDQVW","created_at":"2026-07-05T02:39:24.914637+00:00"},{"alias_kind":"pith_short_8","alias_value":"4BMQMU3U","created_at":"2026-07-05T02:39:24.914637+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20752","citing_title":"Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC","json":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC.json","graph_json":"https://pith.science/api/pith-number/4BMQMU3UDPLIDQVWUQKCE5TDNC/graph.json","events_json":"https://pith.science/api/pith-number/4BMQMU3UDPLIDQVWUQKCE5TDNC/events.json","paper":"https://pith.science/paper/4BMQMU3U"},"agent_actions":{"view_html":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC","download_json":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC.json","view_paper":"https://pith.science/paper/4BMQMU3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.04839&json=true","fetch_graph":"https://pith.science/api/pith-number/4BMQMU3UDPLIDQVWUQKCE5TDNC/graph.json","fetch_events":"https://pith.science/api/pith-number/4BMQMU3UDPLIDQVWUQKCE5TDNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC/action/storage_attestation","attest_author":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC/action/author_attestation","sign_citation":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC/action/citation_signature","submit_replication":"https://pith.science/pith/4BMQMU3UDPLIDQVWUQKCE5TDNC/action/replication_record"}},"created_at":"2026-07-05T02:39:24.914637+00:00","updated_at":"2026-07-05T02:39:24.914637+00:00"}