{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:U7M4ROZ5FM2I7FK3X7R2374BQX","short_pith_number":"pith:U7M4ROZ5","schema_version":"1.0","canonical_sha256":"a7d9c8bb3d2b348f955bbfe3adff8185c789b0df1973f056347656078c737a65","source":{"kind":"arxiv","id":"2503.16693","version":1},"attestation_state":"computed","paper":{"title":"ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Bolin Shen, Shibo Li, Tianming Sha, Yuan Gao, Yushun Dong, Zhan Cheng","submitted_at":"2025-03-20T20:25:32Z","abstract_excerpt":"Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs), where adversaries reconstruct surrogate models by querying the victim model. Existing defense mechanisms, such as watermarking and fingerprinting, suffer from poor real-time performance, susceptibility to evasion, or reliance on post-attack verification, making them inadequate for handling the dynamic characteristics of graph-based MEA variants. To address these limitations, we propose ATOM, a novel real-ti"},"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":"2503.16693","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-20T20:25:32Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1b98cc5800259814e6d8431fe1900f6baaf9f7aa3891378b309997d065fb1146","abstract_canon_sha256":"bbe49e17999f2099ae1c7fbfce08282f0517eb0f7640ac06a1b23bb5f9141dbb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:36:47.726506Z","signature_b64":"o+PyGO0zR7gZWSuiFYYbZ92hxepPjzOIaOAHEvX1G7Fj8kDal+5BTlJZ8T2CdakawWefbmBNemXUX3X/18xeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7d9c8bb3d2b348f955bbfe3adff8185c789b0df1973f056347656078c737a65","last_reissued_at":"2026-07-05T10:36:47.725879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:36:47.725879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Bolin Shen, Shibo Li, Tianming Sha, Yuan Gao, Yushun Dong, Zhan Cheng","submitted_at":"2025-03-20T20:25:32Z","abstract_excerpt":"Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs), where adversaries reconstruct surrogate models by querying the victim model. Existing defense mechanisms, such as watermarking and fingerprinting, suffer from poor real-time performance, susceptibility to evasion, or reliance on post-attack verification, making them inadequate for handling the dynamic characteristics of graph-based MEA variants. To address these limitations, we propose ATOM, a novel real-ti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16693","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/2503.16693/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":"2503.16693","created_at":"2026-07-05T10:36:47.725947+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.16693v1","created_at":"2026-07-05T10:36:47.725947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16693","created_at":"2026-07-05T10:36:47.725947+00:00"},{"alias_kind":"pith_short_12","alias_value":"U7M4ROZ5FM2I","created_at":"2026-07-05T10:36:47.725947+00:00"},{"alias_kind":"pith_short_16","alias_value":"U7M4ROZ5FM2I7FK3","created_at":"2026-07-05T10:36:47.725947+00:00"},{"alias_kind":"pith_short_8","alias_value":"U7M4ROZ5","created_at":"2026-07-05T10:36:47.725947+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.30470","citing_title":"Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX","json":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX.json","graph_json":"https://pith.science/api/pith-number/U7M4ROZ5FM2I7FK3X7R2374BQX/graph.json","events_json":"https://pith.science/api/pith-number/U7M4ROZ5FM2I7FK3X7R2374BQX/events.json","paper":"https://pith.science/paper/U7M4ROZ5"},"agent_actions":{"view_html":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX","download_json":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX.json","view_paper":"https://pith.science/paper/U7M4ROZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.16693&json=true","fetch_graph":"https://pith.science/api/pith-number/U7M4ROZ5FM2I7FK3X7R2374BQX/graph.json","fetch_events":"https://pith.science/api/pith-number/U7M4ROZ5FM2I7FK3X7R2374BQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX/action/storage_attestation","attest_author":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX/action/author_attestation","sign_citation":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX/action/citation_signature","submit_replication":"https://pith.science/pith/U7M4ROZ5FM2I7FK3X7R2374BQX/action/replication_record"}},"created_at":"2026-07-05T10:36:47.725947+00:00","updated_at":"2026-07-05T10:36:47.725947+00:00"}