{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GNIYWT4VSAKIKW6M7SNZ4MZ34M","short_pith_number":"pith:GNIYWT4V","schema_version":"1.0","canonical_sha256":"33518b4f959014855bccfc9b9e333be31e5d4395e63d65f72370b902c672a136","source":{"kind":"arxiv","id":"2202.08602","version":3},"attestation_state":"computed","paper":{"title":"Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Cheng Zhang, Guoxing Chen, Haojin Zhu, Minhui Xue, Shaofeng Li, Zirui Peng","submitted_at":"2022-02-17T11:29:50Z","abstract_excerpt":"In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive l"},"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":"2202.08602","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-02-17T11:29:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1bc38cf58a9fe702ff3dcec20633cb8c49f819bff46a573834fb5c0ddf4a278","abstract_canon_sha256":"7fd6ee7020af284757f59293bde06f8a79cf2c2a4762b14c5e289ef914562fce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:21:09.722637Z","signature_b64":"ZNTbRfGh2sKDJap2ngorEWn9fL7m9bc6vRjlQT/YnKj8NMCkj++n7eO0puYgdzic+Z6mEgMGRAam4zW8gG1MAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33518b4f959014855bccfc9b9e333be31e5d4395e63d65f72370b902c672a136","last_reissued_at":"2026-07-05T04:21:09.722137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:21:09.722137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Cheng Zhang, Guoxing Chen, Haojin Zhu, Minhui Xue, Shaofeng Li, Zirui Peng","submitted_at":"2022-02-17T11:29:50Z","abstract_excerpt":"In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.08602","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/2202.08602/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":"2202.08602","created_at":"2026-07-05T04:21:09.722198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.08602v3","created_at":"2026-07-05T04:21:09.722198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.08602","created_at":"2026-07-05T04:21:09.722198+00:00"},{"alias_kind":"pith_short_12","alias_value":"GNIYWT4VSAKI","created_at":"2026-07-05T04:21:09.722198+00:00"},{"alias_kind":"pith_short_16","alias_value":"GNIYWT4VSAKIKW6M","created_at":"2026-07-05T04:21:09.722198+00:00"},{"alias_kind":"pith_short_8","alias_value":"GNIYWT4V","created_at":"2026-07-05T04:21:09.722198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.11548","citing_title":"Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends","ref_index":116,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M","json":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M.json","graph_json":"https://pith.science/api/pith-number/GNIYWT4VSAKIKW6M7SNZ4MZ34M/graph.json","events_json":"https://pith.science/api/pith-number/GNIYWT4VSAKIKW6M7SNZ4MZ34M/events.json","paper":"https://pith.science/paper/GNIYWT4V"},"agent_actions":{"view_html":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M","download_json":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M.json","view_paper":"https://pith.science/paper/GNIYWT4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.08602&json=true","fetch_graph":"https://pith.science/api/pith-number/GNIYWT4VSAKIKW6M7SNZ4MZ34M/graph.json","fetch_events":"https://pith.science/api/pith-number/GNIYWT4VSAKIKW6M7SNZ4MZ34M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M/action/storage_attestation","attest_author":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M/action/author_attestation","sign_citation":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M/action/citation_signature","submit_replication":"https://pith.science/pith/GNIYWT4VSAKIKW6M7SNZ4MZ34M/action/replication_record"}},"created_at":"2026-07-05T04:21:09.722198+00:00","updated_at":"2026-07-05T04:21:09.722198+00:00"}