{"as_of":"2026-08-09T04:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3ec9dc79dc4c0d50eaabf47d9c53c31d6f14fbf54ebcfcfe80f695f6b1b0a401","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:49:48.145267Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":20,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.12903","last_updated":"2020-10-31T15:19:17Z","snapshot_observed_at":"2026-07-06T08:32:54.818165Z","submitted_at":"2019-10-28T18:39:49Z","title":"IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.12903","snapshot_observed_at":"2026-08-07T14:49:48.145267Z","title":"IPGuard: Protecting intellectual property of deep neural networks via fingerprinting the classification boundary,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.17579","last_updated":"2025-07-30T09:06:26Z","snapshot_observed_at":"2026-08-07T14:42:04.103834Z","submitted_at":"2025-05-23T07:40:34Z","title":"Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:49:48.145267Z"},"links":{"cited_paper":"/paper/1910.12903","citing_paper":"/paper/2505.17579"},"observation_digest":"sha256:e50c8386a3c4caf98c4b46fbf2a2f3404ea8d9d3aadcdc2c5531efbb60c64f0b","observation_id":"5f1e14f7-a77a-4e3e-af38-d40ce50294c2","resolution":{"observed_at":"2026-08-07T14:49:48.145267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.12903","last_updated":"2020-10-31T15:19:17Z","snapshot_observed_at":"2026-07-06T08:32:54.818165Z","submitted_at":"2019-10-28T18:39:49Z","title":"IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary","version":5},"cited_work":{"arxiv_id":"1910.12903","doi":"10.48550/arxiv.1910.12903","metadata_source":"arxiv_reference","pith_arxiv_id":"1910.12903","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"2d7db25e-f622-4eaf-9cbc-d57fd88fce8a","year":2019},"citing_paper":{"arxiv_id":"2508.11548","last_updated":"2026-04-07T07:06:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-15T15:50:20Z","title":"Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-18T22:45:31.935618Z"},"links":{"cited_paper":"/paper/1910.12903","citing_paper":"/paper/2508.11548"},"observation_digest":"sha256:f9ad6c0c6dcae2af4fda687b715cfd3a5a48453493bed4809b46c91dd9e8eeca","observation_id":"e13126fa-e1f9-424c-b833-021bfe3b4ff1","resolution":{"observed_at":"2026-05-18T22:46:52.591232Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.12903","last_updated":"2020-10-31T15:19:17Z","snapshot_observed_at":"2026-07-06T08:32:54.818165Z","submitted_at":"2019-10-28T18:39:49Z","title":"IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary","version":5},"cited_work":{"arxiv_id":"1910.12903","doi":"10.48550/arxiv.1910.12903","metadata_source":"arxiv_reference","pith_arxiv_id":"1910.12903","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"2d7db25e-f622-4eaf-9cbc-d57fd88fce8a","year":2019},"citing_paper":{"arxiv_id":"2605.27148","last_updated":"2026-06-01T21:00:09Z","snapshot_observed_at":"2026-07-06T23:36:53.330986Z","submitted_at":"2026-05-26T15:10:35Z","title":"Landseer: Exploring the Machine Learning Defense Landscape","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T17:27:29.241219Z"},"links":{"cited_paper":"/paper/1910.12903","citing_paper":"/paper/2605.27148"},"observation_digest":"sha256:909fbdccd1f7b387cddaa03e98cee313ab63345f58a071751343d7e395a6087b","observation_id":"d5187d00-1009-47c0-8223-dbaa96bc2dac","resolution":{"observed_at":"2026-06-29T17:33:45.391704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1910.12903/citation-record","integrity":"/paper/1910.12903/integrity","json":"/paper/1910.12903/citation-record.json","paper":"/paper/1910.12903"},"outbound":[],"paper":{"arxiv_id":"1910.12903","last_updated":"2020-10-31T15:19:17Z","latest_version":5,"primary_category":"cs.CR","snapshot_observed_at":"2026-07-06T08:32:54.818165Z","submitted_at":"2019-10-28T18:39:49Z","title":"IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1910.12903."}