{"as_of":"2026-08-07T12:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca201ed4362d611bdfd3d430fdae4be2379bd32293159f64983af73f71b9b277","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T21:52:49.514513Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2602.18934/citation-record","integrity":"/paper/2602.18934/integrity","json":"/paper/2602.18934/citation-record.json","paper":"/paper/2602.18934"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.478229Z","title":"Privacy-preserving machine learning for healthcare: open challenges and future perspectives,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.478229Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:88e1c7e0abc6b0eaf9a6cd16d253d6aca99387628244bdc4d1caa890ff3247a4","observation_id":"d61c60a8-a5eb-4d5c-8fa3-109c16df2461","resolution":{"observed_at":"2026-08-02T21:52:45.478229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.550819Z","title":"Machine learning as a service (mlaas)—an enterprise perspective,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.550819Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:e036bd33b613a839b7f3699f568ed28edc588cfd976a6dc2b000189d53be9c08","observation_id":"97b6a9db-ed88-432d-9c01-5a24194519a1","resolution":{"observed_at":"2026-08-02T21:52:45.550819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.647380Z","title":"Membership inference attacks against machine learning models,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.647380Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:946a97e1a869fa3730c6609537d0ea8188c5ed43c87c14cfbb27d0bb0489389c","observation_id":"533bbcee-267e-4a56-adb4-5fb8ed5212fb","resolution":{"observed_at":"2026-08-02T21:52:45.647380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.713131Z","title":"Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.713131Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:5ede165022b1fe508db79ef34e55b3661b32ad2cf33d6841de4d390c110e3908","observation_id":"26344536-7625-47f6-b66a-b2f204db5aae","resolution":{"observed_at":"2026-08-02T21:52:45.713131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.788323Z","title":"Knock knock, who’s there? membership inference on aggregate location data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.788323Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:9c6f2193c17eb76467423080e70dd5d3aab7c12f4a46323c00711c4f7adf3aa0","observation_id":"1a83376f-cdc4-4ac5-9fed-4063cb6c722c","resolution":{"observed_at":"2026-08-02T21:52:45.788323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.845547Z","title":"Demystifying membership inference attacks in machine learning as a service,","venue":null,"work_id":null,"year":2073},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.845547Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:f6e34a974a4f2cd11275192d075decd5d09ced501d818281307e849752dd0ef1","observation_id":"38233162-dd75-43ff-9809-11df0790bdb6","resolution":{"observed_at":"2026-08-02T21:52:45.845547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.905327Z","title":"LOGAN: membership inference attacks against generative models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.905327Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:a687bc089d9e262c325d43c85ed8b6cbc7b485dbc3e3dd54f18e98597a076d0d","observation_id":"592ccc66-975b-4db2-afc2-b213cd815f19","resolution":{"observed_at":"2026-08-02T21:52:45.905327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:45.970530Z","title":"Monte carlo and reconstruction membership inference attacks against generative models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:45.970530Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:25b3e8a41ad04e712c3ad1c56ed058ec2c154d704624358da868d8d51c34513d","observation_id":"d8976221-d810-41b1-8501-992650251908","resolution":{"observed_at":"2026-08-02T21:52:45.970530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.086606Z","title":"Privacy risks of securing machine learning models against adversarial examples,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.086606Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:55b40aa081e4662a8ff98fba2a070ad9553e4394bc713dade1502663e57abe41","observation_id":"0d5867ef-25af-45af-b2c5-8351e4bb34ba","resolution":{"observed_at":"2026-08-02T21:52:46.086606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.162354Z","title":"White-box vs black-box: Bayes optimal strategies for membership inference,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.162354Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:66c1f3a4a196256ecf663b2db32282a90d8164140087152bd68061d2a899179e","observation_id":"3dd54fdf-b3c7-4142-a027-1e682c62da53","resolution":{"observed_at":"2026-08-02T21:52:46.162354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.249576Z","title":"A pragmatic approach to membership inferences on machine learning models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.249576Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:22f510bfb63fc7da5635c30d8a6a27eafd8429237c728430b0d5d30ba10d4b76","observation_id":"b6661713-8124-4f79-9429-917948f14140","resolution":{"observed_at":"2026-08-02T21:52:46.249576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.367503Z","title":"Membership inference attacks and defenses in classification models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.367503Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:9d3eaa0dfa1a37d41d4aa795f5f9f916748dc3fe47ecfdf2d6116f2f115adab0","observation_id":"395e5ce9-366c-47c1-aeee-9451868803f8","resolution":{"observed_at":"2026-08-02T21:52:46.367503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.445516Z","title":"Practical blind membership inference attack via differential comparisons,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.445516Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:868152e7c86ccca50a68d125516d3d57ee5e8a1294585b764b1a576c4474a04c","observation_id":"0661799e-53a7-4e50-ada2-924e0abd279d","resolution":{"observed_at":"2026-08-02T21:52:46.445516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.521769Z","title":"Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.521769Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:e1c23874fc944531bcbac16b4d18a7a2b7e8b916784e5c62ad3d8b601b58d8fb","observation_id":"0c131947-99c7-488b-8382-054659ebe5a4","resolution":{"observed_at":"2026-08-02T21:52:46.521769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.599547Z","title":"Memguard: Defending against black-box membership inference attacks via adversarial examples,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.599547Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:2feb8f83c9a26c792511a9b50c886a8b2241931b3cd9b1056ea319141ea6f372","observation_id":"522ac8e6-cbbd-4d49-a4d9-75f9d7764499","resolution":{"observed_at":"2026-08-02T21:52:46.599547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.03915","last_updated":"2020-08-20T16:27:41Z","snapshot_observed_at":"2026-07-06T09:18:41.906868Z","submitted_at":"2020-05-08T09:07:38Z","title":"Defending Model Inversion and Membership Inference Attacks via Prediction Purification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.03915","snapshot_observed_at":"2026-08-02T21:52:46.661861Z","title":"Defending model inversion and membership inference attacks via prediction purification,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.661861Z"},"links":{"cited_paper":"/paper/2005.03915","citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:e428155ccbb94c297d1c4b08c4ca3ff8739366f25e9c4b7de00482ad53b997c5","observation_id":"8cba450f-6832-48fd-befc-41e25cde641c","resolution":{"observed_at":"2026-08-02T21:52:46.661861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.773916Z","title":"Deep learning with differential privacy,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.773916Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:0120aba277b21344e27e8adf7322f7506bd30e6240cb0b2143461f46cf4de12b","observation_id":"a76043e3-ec1f-45bd-bc88-6057ca396fc6","resolution":{"observed_at":"2026-08-02T21:52:46.773916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.889981Z","title":"Dropout: a simple way to prevent neural networks from overfitting,","venue":null,"work_id":null,"year":1929},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.889981Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:9571538c74945e04e767f8d2fd7a1f4cb0044f5c4094aafc9f912768343bc840","observation_id":"a84801f8-72db-4904-88a7-fea1fc6773b1","resolution":{"observed_at":"2026-08-02T21:52:46.889981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:46.998312Z","title":"Low-cost high-power membership inference attacks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:46.998312Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:012f0d845d72a3686e6e257a1f69ea471d78427e331fcf77715f080df0fb67b2","observation_id":"93f9709b-8b33-42c3-87d9-63a5e0cbb444","resolution":{"observed_at":"2026-08-02T21:52:46.998312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07841","last_updated":"2024-09-16T13:18:23Z","snapshot_observed_at":"2026-08-07T03:58:21.441544Z","submitted_at":"2024-02-12T17:52:05Z","title":"Do Membership Inference Attacks Work on Large Language Models?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07841","snapshot_observed_at":"2026-08-02T21:52:47.078915Z","title":"Do membership inference attacks work on large language models?,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.078915Z"},"links":{"cited_paper":"/paper/2402.07841","citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:b8a0f942f682665cb8d4107eb1f182011f6bebbe31680f15a27d9977ede88d75","observation_id":"42f4daff-5c49-4577-8783-97d08928e89a","resolution":{"observed_at":"2026-08-02T21:52:47.078915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.217591Z","title":"Quantifying privacy risks of masked language models using membership inference attacks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.217591Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:d9f80c47e2b870fe0dc54a57e3e6227a0d5b5293b3fa2253bb14b4aff35ec9ca","observation_id":"201db473-f1dd-4dbd-838e-82b3dafecce1","resolution":{"observed_at":"2026-08-02T21:52:47.217591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.342547Z","title":"Membership inference attacks against language models via neighbourhood comparison,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.342547Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:144080a9e7c95862a3dea3a0331da7c531fc5fdd2147aff4c81a28648ce2c0eb","observation_id":"4f21cd3d-eaff-45d6-a15b-70132c4e3f29","resolution":{"observed_at":"2026-08-02T21:52:47.342547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.446301Z","title":"Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack ,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.446301Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:10389a21f4c9acbc895699cec366b80edebdc999339466d620dc9ac71e331fd6","observation_id":"b01041dc-9b2e-4242-8d5c-e01053fe157f","resolution":{"observed_at":"2026-08-02T21:52:47.446301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.538964Z","title":"Enhanced membership inference attacks against machine learning models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.538964Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:b3f5f9764298322b04c75b2ab285416db638f2605cab461c1e924051ee642a1e","observation_id":"4eaa8328-ce62-41c0-adbb-5854d03f3fdc","resolution":{"observed_at":"2026-08-02T21:52:47.538964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.643031Z","title":"Membership inference attacks from first principles,","venue":null,"work_id":null,"year":1914},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.643031Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:39e1dee47a2048af90142fdcbe832daf96ca1bb13cfd99ed6a93130e065bb036","observation_id":"eb3d8445-383c-46e0-86a3-45ce8cc0445d","resolution":{"observed_at":"2026-08-02T21:52:47.643031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.767552Z","title":"Label-only membership inference attacks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.767552Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:89e4783dc8f56d7f617acb939ff85a73388d8e4b219a9c49227314700bacf93e","observation_id":"64693ebe-07a8-462d-b831-27b4afaf312f","resolution":{"observed_at":"2026-08-02T21:52:47.767552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.850972Z","title":"Membership leakage in label-only exposures,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.850972Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:0444806a19c67fca9928b2fd26f07d25bab6f418a3bc552878f51b64afaee273","observation_id":"f9155baf-720d-4bc2-ae6c-83c8e7fec025","resolution":{"observed_at":"2026-08-02T21:52:47.850972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:47.929043Z","title":"Privacy risk in machine learning: Analyzing the connection to overfitting,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:47.929043Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:b959b8f3e24029bfa85fd0261dcf604e388ad8c4412bac2d05d9a1a604ea7df5","observation_id":"aab8e15c-5a8d-4862-b83d-12410efa7c46","resolution":{"observed_at":"2026-08-02T21:52:47.929043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.003905Z","title":"Stealing machine learning models via prediction apis,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.003905Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:74e70c75b4fc6bcbdd431d1c8243fd90f3c16f094eaf17dede5fef16a1cc278f","observation_id":"f9309ac3-43da-429d-9b3b-792283436945","resolution":{"observed_at":"2026-08-02T21:52:48.003905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.079095Z","title":"Knockoff nets: Stealing functionality of black-box models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.079095Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:85acff347204f8fd6637b2a091892dc6480c7981747916c04c1ab1fc02eb1196","observation_id":"71bbad5b-1424-4761-a9ab-d6bc80192ca0","resolution":{"observed_at":"2026-08-02T21:52:48.079095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.162394Z","title":"PRADA: protecting against DNN model stealing attacks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.162394Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:cd4bc859320459a03cafc0c51aa621d1346132a7c8ed33294eee2f6997da6ddd","observation_id":"717902f3-aa40-4954-8e79-20b58dddb938","resolution":{"observed_at":"2026-08-02T21:52:48.162394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.255726Z","title":"Practical black-box attacks against machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.255726Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:efef0294d8405defddc1165f36e21ae30778f5e43954c77d8b6b81f7ae91ee3a","observation_id":"d79796f9-d090-4a78-80ed-5ae0f3853b6a","resolution":{"observed_at":"2026-08-02T21:52:48.255726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.334749Z","title":"High accuracy and high fidelity extraction of neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.334749Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:5e558a304c8ce726fa8647e12ee85b3dd4bba0e91eabb65861ddf82f91bffee4","observation_id":"7a3c7ac0-0d57-4897-9797-ecd079deafc8","resolution":{"observed_at":"2026-08-02T21:52:48.334749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.384744Z","title":"Data-free model extraction,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.384744Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:acb206d67c0c67d0e2f5886cfa59bda6302aeb819d0aa8b9e85adcf5acf354dd","observation_id":"4ada4d62-048f-4687-882f-692ac0efa725","resolution":{"observed_at":"2026-08-02T21:52:48.384744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.484747Z","title":"Thieves on sesame street! model extraction of bert-based apis,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.484747Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:d3fd48c524fb02fb48f4eb6686654686412c2095cf214c6b1878c6d5ffe50875","observation_id":"9ca441d8-ffac-4b6d-bea5-1924a93a8f09","resolution":{"observed_at":"2026-08-02T21:52:48.484747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.594747Z","title":"Marich: A query-efficient distributionally equivalent model extraction attack,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.594747Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:78ecd93880ad795f16d159a49284e3274db0a0de133ff5d757fd5697f5332e93","observation_id":"e9372b4a-ea64-4fe3-b998-6df446be2468","resolution":{"observed_at":"2026-08-02T21:52:48.594747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.760537Z","title":"AUTOLYCUS: exploiting explainable artificial intelligence (XAI) for model extraction attacks against interpretable models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.760537Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:16f62a1dd10dfbae4a86ccee106100df2d8fd51b3514bce99acabd460437f5a5","observation_id":"cf9d8dbe-dbce-48a6-bbc9-ce495ad03229","resolution":{"observed_at":"2026-08-02T21:52:48.760537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.842225Z","title":"Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.842225Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:297d7b5712ea3732fbbd671ee50c6d298ccf46b6db5f93023ee77b1a2c8bb66c","observation_id":"3abb503e-3e55-4c4f-95cb-7c09fbe6df78","resolution":{"observed_at":"2026-08-02T21:52:48.842225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.917355Z","title":"Delving into transferable adversarial examples and black-box attacks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.917355Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:c93fc601ea17c51511914b3ec1de78bd9bdab218ec145e2c19a2a1da5f5e07c9","observation_id":"6c3729d8-bf0e-4e58-94af-6fa5d8d75337","resolution":{"observed_at":"2026-08-02T21:52:48.917355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:48.986921Z","title":"Cross-domain transferability of adversarial perturbations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:48.986921Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:57217f61111b030fcd622bdbba51a5e27f433e865b9e7284643b2200d465cc13","observation_id":"3fde08ee-5e3e-4350-aec0-adeae5bac392","resolution":{"observed_at":"2026-08-02T21:52:48.986921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.094752Z","title":"Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.094752Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:a4ea72eef659bd8ee4a28536adb5789b51522991941a29a0e40b962c10cc30f8","observation_id":"4f61897d-a83a-4c16-93f0-538d6c3c410e","resolution":{"observed_at":"2026-08-02T21:52:49.094752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.152644Z","title":"Feature selection, l1 vs. l2 regularization, and rotational invariance,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.152644Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:53112f0150d584551e7422adb4b90c8f43df901b9679f2bbde4cc58c0df6b5c7","observation_id":"55bf247e-dc27-413e-9cd9-8279ebef8668","resolution":{"observed_at":"2026-08-02T21:52:49.152644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.247130Z","title":"A simple weight decay can improve generalization,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.247130Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:91216ef42240fb0e7ff31ecde957fbff0ae18e6d11942ceb1e1eb76fde931295","observation_id":"72f84d1e-db54-4b3a-92bd-bc2137dd37a8","resolution":{"observed_at":"2026-08-02T21:52:49.247130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.292420Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.292420Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:972a1593756435e292719ec62b6e8b92851c3ab40f68a6faf38492ca264c1668","observation_id":"2d80519a-649f-4da5-b98d-13a9f2f2323b","resolution":{"observed_at":"2026-08-02T21:52:49.292420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.407299Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.407299Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:d7d8aaa8b813d85937b522c3a7d6a5f59e4218e616e0804f420e1d90693cd402","observation_id":"11787e36-2d05-452d-8ca6-5a594f258595","resolution":{"observed_at":"2026-08-02T21:52:49.407299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:52:49.514513Z","title":"Adversarial robustness toolbox v1.0.0,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T21:52:49.514513Z"},"links":{"citing_paper":"/paper/2602.18934"},"observation_digest":"sha256:fd412ea7f92fd0057adfc2440b75429a36f22d7e506377bd2dde61dcd940ddf6","observation_id":"e90e2752-f215-4cc5-905a-c336d97abfeb","resolution":{"observed_at":"2026-08-02T21:52:49.514513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.18934","last_updated":"2026-06-23T14:43:29Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T21:52:44.872784Z","submitted_at":"2026-02-21T18:57:17Z","title":"LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":46,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":46},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2602.18934."}