{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZSX576TBV3WRMWFKDJUCRNDFQ3","short_pith_number":"pith:ZSX576TB","schema_version":"1.0","canonical_sha256":"ccafdffa61aeed1658aa1a6828b46586efee186a4e1668ae5c83871f91338170","source":{"kind":"arxiv","id":"2207.00425","version":3},"attestation_state":"computed","paper":{"title":"Transferable Graph Backdoor Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Bao Gia Doan, Damith C. Ranasinghe, Olivier De Vel, Paul Montague, Salil S. Kanhere, Seyit Camtepe, Shuiqiao Yang, Tamas Abraham","submitted_at":"2022-06-21T06:25:37Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are found to be vulnerable to unnoticeable perturbations on both graph structure and node features. Many adversarial attacks have been proposed to disclose the fragility of GNNs under different perturbation strategies to create adversarial examples. However, vulnerability of GNNs to succ"},"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":"2207.00425","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-06-21T06:25:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a0d15b633457e1a7886507fc1d6ef27ef6e1b07b1b8438de6c12f516a1d1314c","abstract_canon_sha256":"236c77bd5bc704d6f3462ac9adc0ac55af460f4414d25aa28fe5837ad7c79113"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:41.607024Z","signature_b64":"2b8efYbAc1AdKE4b9UXEcZBgWSMssCTQ6WHJjuQyvHfhalmBlzKKZ/zqdCkGvD8xPFOytHJChe0lgBCd/x2LBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccafdffa61aeed1658aa1a6828b46586efee186a4e1668ae5c83871f91338170","last_reissued_at":"2026-07-05T04:37:41.606651Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:41.606651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transferable Graph Backdoor Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Bao Gia Doan, Damith C. Ranasinghe, Olivier De Vel, Paul Montague, Salil S. Kanhere, Seyit Camtepe, Shuiqiao Yang, Tamas Abraham","submitted_at":"2022-06-21T06:25:37Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are found to be vulnerable to unnoticeable perturbations on both graph structure and node features. Many adversarial attacks have been proposed to disclose the fragility of GNNs under different perturbation strategies to create adversarial examples. However, vulnerability of GNNs to succ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.00425","kind":"arxiv","version":3},"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/2207.00425/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":"2207.00425","created_at":"2026-07-05T04:37:41.606707+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.00425v3","created_at":"2026-07-05T04:37:41.606707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.00425","created_at":"2026-07-05T04:37:41.606707+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZSX576TBV3WR","created_at":"2026-07-05T04:37:41.606707+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZSX576TBV3WRMWFK","created_at":"2026-07-05T04:37:41.606707+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZSX576TB","created_at":"2026-07-05T04:37:41.606707+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/ZSX576TBV3WRMWFKDJUCRNDFQ3","json":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3.json","graph_json":"https://pith.science/api/pith-number/ZSX576TBV3WRMWFKDJUCRNDFQ3/graph.json","events_json":"https://pith.science/api/pith-number/ZSX576TBV3WRMWFKDJUCRNDFQ3/events.json","paper":"https://pith.science/paper/ZSX576TB"},"agent_actions":{"view_html":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3","download_json":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3.json","view_paper":"https://pith.science/paper/ZSX576TB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.00425&json=true","fetch_graph":"https://pith.science/api/pith-number/ZSX576TBV3WRMWFKDJUCRNDFQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/ZSX576TBV3WRMWFKDJUCRNDFQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3/action/storage_attestation","attest_author":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3/action/author_attestation","sign_citation":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3/action/citation_signature","submit_replication":"https://pith.science/pith/ZSX576TBV3WRMWFKDJUCRNDFQ3/action/replication_record"}},"created_at":"2026-07-05T04:37:41.606707+00:00","updated_at":"2026-07-05T04:37:41.606707+00:00"}