{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6ANPCDVL7VW5N3JERE2PLXK2PI","short_pith_number":"pith:6ANPCDVL","schema_version":"1.0","canonical_sha256":"f01af10eabfd6dd6ed248934f5dd5a7a0dbbdc3e796d513654bc1d5b84ba9bf3","source":{"kind":"arxiv","id":"2401.08261","version":2},"attestation_state":"computed","paper":{"title":"Probabilistically Robust Watermarking of Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Ivan Oseledets, Mikhail Pautov, Nikita Bogdanov, Oleg Rogov, Stanislav Pyatkin","submitted_at":"2024-01-16T10:32:13Z","abstract_excerpt":"As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model. Unfortunately, these methods usually produce watermarks susceptible to model stealing attacks. In our research, we introduce a novel trigger set-based watermarking approach that demonstrates resilience against functionality stealing attacks, particularly those involving extraction and distillation. Our approach does not require additional model training "},"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":"2401.08261","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-01-16T10:32:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aa728b6259dbc9ead8be6994510f04ba49fdbe535acc3b87181820cfc4806bb1","abstract_canon_sha256":"45ff3cddf83d382066d2ba558505e8ac2fd37a77ffaaa3a1bedf5f98ba782059"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:23.455550Z","signature_b64":"or7Hlf2YTM6Gp74ALgWeKAX11s1rPvT7FQFHhY1YZbYDRT30hulPM7HJbdf7ZnS1YflfriFQG8owLhcFJvlMCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f01af10eabfd6dd6ed248934f5dd5a7a0dbbdc3e796d513654bc1d5b84ba9bf3","last_reissued_at":"2026-07-05T09:38:23.454982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:23.454982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistically Robust Watermarking of Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Ivan Oseledets, Mikhail Pautov, Nikita Bogdanov, Oleg Rogov, Stanislav Pyatkin","submitted_at":"2024-01-16T10:32:13Z","abstract_excerpt":"As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model. Unfortunately, these methods usually produce watermarks susceptible to model stealing attacks. In our research, we introduce a novel trigger set-based watermarking approach that demonstrates resilience against functionality stealing attacks, particularly those involving extraction and distillation. Our approach does not require additional model training "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08261","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/2401.08261/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":"2401.08261","created_at":"2026-07-05T09:38:23.455049+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.08261v2","created_at":"2026-07-05T09:38:23.455049+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08261","created_at":"2026-07-05T09:38:23.455049+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ANPCDVL7VW5","created_at":"2026-07-05T09:38:23.455049+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ANPCDVL7VW5N3JE","created_at":"2026-07-05T09:38:23.455049+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ANPCDVL","created_at":"2026-07-05T09:38:23.455049+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/6ANPCDVL7VW5N3JERE2PLXK2PI","json":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI.json","graph_json":"https://pith.science/api/pith-number/6ANPCDVL7VW5N3JERE2PLXK2PI/graph.json","events_json":"https://pith.science/api/pith-number/6ANPCDVL7VW5N3JERE2PLXK2PI/events.json","paper":"https://pith.science/paper/6ANPCDVL"},"agent_actions":{"view_html":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI","download_json":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI.json","view_paper":"https://pith.science/paper/6ANPCDVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.08261&json=true","fetch_graph":"https://pith.science/api/pith-number/6ANPCDVL7VW5N3JERE2PLXK2PI/graph.json","fetch_events":"https://pith.science/api/pith-number/6ANPCDVL7VW5N3JERE2PLXK2PI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI/action/storage_attestation","attest_author":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI/action/author_attestation","sign_citation":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI/action/citation_signature","submit_replication":"https://pith.science/pith/6ANPCDVL7VW5N3JERE2PLXK2PI/action/replication_record"}},"created_at":"2026-07-05T09:38:23.455049+00:00","updated_at":"2026-07-05T09:38:23.455049+00:00"}