{"as_of":"2026-08-12T03:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0976212a2670b90aaded0501f244ec1d295ea749adc020b2594b52ae39a349e7","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:01:30.986456Z","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":"2026-08-12T02:21:56.303337Z","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":"2026-08-11T14:01:30.986456Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12563","last_updated":"2024-12-17T05:46:50Z","snapshot_observed_at":"2026-08-11T13:55:14.560249Z","submitted_at":"2024-12-17T05:46:50Z","title":"Task-Agnostic Language Model Watermarking via High Entropy Passthrough Layers","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T14:01:30.986456Z"},"links":{"cited_paper":"/paper/1910.12903","citing_paper":"/paper/2412.12563"},"observation_digest":"sha256:9ac0111624bcaae6c2800de5850cf90cc916e238db24fad5fbbc2ae1ae0e9d69","observation_id":"7be50216-3973-45ff-b3cf-07f1ec6be1c0","resolution":{"observed_at":"2026-08-11T14:01:30.986456Z","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":"2026-08-12T02:21:56.303337Z","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":"2026-08-10T01:22:36.560324Z","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":"sha256:6eb49ae62ffb8b7438b44aba9335e7ca8dc694511c33adcf7bbb58329444bad0","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":"2026-08-12T02:21:56.303337Z","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":"sha256:0a1b1055e0d5e4ddc57d5c39655708ae7c4e1b5d7ab5c281e1c0ad67076139fe","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 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.12903","last_updated":"2020-10-31T15:19:17Z","snapshot_observed_at":"2026-08-12T02:21:56.303337Z","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":"sha256:9899e0b940370c5e5389a038ae4ceec07229be4bbb212684231c4258ed5f9433","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 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-12T02:21:56.303337Z","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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1910.12903."}