{"as_of":"2026-08-14T15:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ebe3fde7d532f7388b3e976df58fa4956af625867f5cba26f57479301b677712","coverage":[{"denominator":93,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":93,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:35:42.293715Z","state":"measured"},{"denominator":93,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":93,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2411.12508/citation-record","integrity":"/paper/2411.12508/integrity","json":"/paper/2411.12508/citation-record.json","paper":"/paper/2411.12508"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T17:35:41.894797Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.894797Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:55e30fab833ac6da3390669d4207e438002e3e486bac406c55c256135d0f6235","observation_id":"1b0b88a6-85d9-41a3-b17a-764121951107","resolution":{"observed_at":"2026-08-12T17:35:41.894797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.05966","last_updated":"2024-02-18T18:32:50Z","snapshot_observed_at":"2026-08-08T14:53:16.868082Z","submitted_at":"2020-08-13T15:22:49Z","title":"Deep-Lock: Secure Authorization for Deep Neural Networks","version":2},"cited_work":{"arxiv_id":"2008.05966","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.05966","snapshot_observed_at":"2026-08-12T17:35:42.583903Z","title":"Deep-Lock: Secure Authorization for Deep Neural Networks","venue":"cs.LG","work_id":"8779a74d-f7a9-424a-9017-422bb3ce2093","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.900001Z"},"links":{"cited_paper":"/paper/2008.05966","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c61f8c1b5b82d111d761a9768b7c830229e34c9ccd7162b393abaa0ffc4e8514","observation_id":"ef826498-a395-4c16-bf6b-3b4f1ca5e41a","resolution":{"observed_at":"2026-08-12T17:35:42.590128Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17274","last_updated":"2022-06-03T17:52:04Z","snapshot_observed_at":"2026-08-14T03:15:48.310538Z","submitted_at":"2022-03-31T17:59:30Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17274","snapshot_observed_at":"2026-08-12T17:35:41.905313Z","title":"Exploring visual prompts for adapting large-scale models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.905313Z"},"links":{"cited_paper":"/paper/2203.17274","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:bbcaa7b731d51bce303364eb650cdddac6822172be338f2f73fc689d622e88ca","observation_id":"8a0df5ab-e561-4cd1-9d06-35d6dc928d38","resolution":{"observed_at":"2026-08-12T17:35:41.905313Z","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":"2026-08-12T17:35:41.909730Z","title":"Probing classifiers: Promises, shortcomings, and advances,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.909730Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:a63d9ad2ddcb55796df71e9ac2b41dbd057a1c9a449f54dec2329fa91b95463a","observation_id":"30fe1fb3-7f44-4812-9e31-db7736f82ea6","resolution":{"observed_at":"2026-08-12T17:35:41.909730Z","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":"2026-08-12T17:35:41.913523Z","title":"Representation learning: A review and new perspec- tives,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.913523Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c234414f5b43604247e4e3c8620c7ec7c727605cd4946c82ea3fa233261feb7e","observation_id":"3723ab20-1f61-4f41-bfaa-678e6fbd2664","resolution":{"observed_at":"2026-08-12T17:35:41.913523Z","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":"2026-08-12T17:35:41.917558Z","title":"Military vehicles dataset,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.917558Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:0c22ad4ea4421337a7cef2bb17e641fad8e33f09f09bdec43818fb479c15e845","observation_id":"6cfe25cf-5f30-49c8-99ec-33c0190848cc","resolution":{"observed_at":"2026-08-12T17:35:41.917558Z","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":"2026-08-12T17:35:41.922044Z","title":"Putting representations to use,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.922044Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:51b9d0829cce8454c116b933c4af017675289074a39bfa66502cbcdb2634fed7","observation_id":"e779c107-bb0d-42c6-baed-e14f69ef7daa","resolution":{"observed_at":"2026-08-12T17:35:41.922044Z","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":"2026-08-12T17:35:41.925923Z","title":"Emerging properties in self-supervised vision trans- formers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.925923Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:a16399bd2bebb97608fe53e5f8d1c907ba443002658c107e78baeb037fccffac","observation_id":"037167b9-aa22-401b-9ebe-57d14c735aca","resolution":{"observed_at":"2026-08-12T17:35:41.925923Z","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":"2026-08-12T17:35:41.929912Z","title":"Hardware-assisted intellectual property protection of deep learning models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.929912Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c7ea519d9d7b3a63eb6bdf0cf5b316fe4518c1351e65cb54bbe2154b4d5c3e2e","observation_id":"67a7c7e6-3574-46d6-97a1-07738aed6082","resolution":{"observed_at":"2026-08-12T17:35:41.929912Z","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":"2026-08-12T17:35:41.933607Z","title":"Confronting the risks of artificial intelligence,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.933607Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:3b119c6ac0ab98b1fd3f9e566426eb907c8200959230aab935e2f7d11484e8df","observation_id":"b0ccf5de-642f-4e4b-9c9d-2d745888f5c9","resolution":{"observed_at":"2026-08-12T17:35:41.933607Z","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":"2026-08-12T17:35:41.937589Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.937589Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c6a694941eff275d1ea3824b01e370d5befb1d7a7ce0c53af700f6590c9ea676","observation_id":"40138d19-f415-441b-b7f7-f533b6c011f5","resolution":{"observed_at":"2026-08-12T17:35:41.937589Z","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":"2026-08-12T17:35:41.941532Z","title":"Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.941532Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d58f796ab258e5b19b476cb51f8b1bd7039ffb37f5554acadbd1419fb9c72fea","observation_id":"de51bf7a-06a1-46f8-9483-9f321d584fe6","resolution":{"observed_at":"2026-08-12T17:35:41.941532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10547","last_updated":"2019-05-31T12:53:15Z","snapshot_observed_at":"2026-07-06T07:35:48.510196Z","submitted_at":"2019-02-27T14:17:12Z","title":"An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10547","snapshot_observed_at":"2026-08-12T17:35:41.945289Z","title":"An embarrassingly simple approach for transfer learning from pretrained language models,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.945289Z"},"links":{"cited_paper":"/paper/1902.10547","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:32b16a1ba2eeb68150c9222bd798bc95efa87a2089fa4228dfe292ca60bcec62","observation_id":"464fd281-e5a2-4aa7-a814-1bfc9fa87c13","resolution":{"observed_at":"2026-08-12T17:35:41.945289Z","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":"2026-08-12T17:35:41.949702Z","title":"general-image-embedding3,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.949702Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7e427705ddd1b988b23655511f06ddeea824d0734e1c59a81f24c94326f15e62","observation_id":"a9ace80d-aedc-47ae-818b-8ff58c87b23e","resolution":{"observed_at":"2026-08-12T17:35:41.949702Z","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":"2026-08-12T17:35:41.953872Z","title":"An analysis of single-layer networks in unsupervised feature learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.953872Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:063ec5795a5755d585257cc3d9c149bb848c1e85105be91668af35aa4ed90aaf","observation_id":"201c3e66-5988-407d-98fd-9278e3f12a38","resolution":{"observed_at":"2026-08-12T17:35:41.953872Z","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":"2026-08-12T17:35:41.958210Z","title":"Emnist: Extending mnist to handwritten letters,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.958210Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b954af55af37c10d98f3c8f30c0d9b98c26b55d8756efdf39288e78cb3433875","observation_id":"fba5c511-a9ce-47dd-836d-08fe772cc2a7","resolution":{"observed_at":"2026-08-12T17:35:41.958210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.03584","last_updated":"2020-01-10T09:06:09Z","snapshot_observed_at":"2026-08-14T08:25:28.870815Z","submitted_at":"2019-11-08T23:48:38Z","title":"On the Relationship between Self-Attention and Convolutional Layers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.03584","snapshot_observed_at":"2026-08-12T17:35:41.962175Z","title":"On the relationship between self-attention and convolutional layers,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.962175Z"},"links":{"cited_paper":"/paper/1911.03584","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e08c4487436508786af7a1cc40bf88820e787eeb83bad3e072c523bf68e09af2","observation_id":"fab1d02a-02c6-431d-9552-5e2028a0b9d9","resolution":{"observed_at":"2026-08-12T17:35:41.962175Z","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":"2026-08-12T17:35:41.966965Z","title":"Supervised learning,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.966965Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c10b62570146ff36c7bad4f9c31ab0aa8006eac4120f02dfa31c338c99cc6250","observation_id":"ddbb787e-a291-49bd-b76b-313e245143ce","resolution":{"observed_at":"2026-08-12T17:35:41.966965Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.470551Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":"e71e05aa-94cd-4c92-a428-f6ca22f4eddb","year":2009},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.971401Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7442c69a644a39c4e35172d3d9bfd1858d4ebcac5d263fb46c15956f78c18f19","observation_id":"cf0d6b29-2189-4d67-96e4-ba599cf4360a","resolution":{"observed_at":"2026-08-12T17:35:43.475075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.457644Z","title":"Non-transferable pruning,","venue":null,"work_id":"49fd9d40-aae2-466a-97c3-8cb2564c65d9","year":2025},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.975639Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:9cf52de5254ee6ccc2b866907da729304a81f139c7e2488daad188a954d51ce5","observation_id":"c8f3f405-f656-44f2-8959-28dfae50d105","resolution":{"observed_at":"2026-08-12T17:35:43.462375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:41.979593Z","title":"Puma: Secure inference of llama-7b in five minutes,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.979593Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:23bf88cc6f2cc698dcba70df96a62b2567518aa95385293a93065a96eac0ed16","observation_id":"bc391998-6678-4fcd-8092-64fc5d6917c9","resolution":{"observed_at":"2026-08-12T17:35:41.979593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-05T23:54:27.386622Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-12T17:35:41.983988Z","title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.983988Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:70b331921649dfa4ee8ca5f4f307cfbb6f44a42ffee9b54906f0e19f66adcb4b","observation_id":"a64992aa-daa1-41c7-91a4-b37f3a1aaf7e","resolution":{"observed_at":"2026-08-12T17:35:41.983988Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.444794Z","title":"Decorate the newcomers: Visual domain prompt for continual test time adaptation,","venue":null,"work_id":"486fae7a-6d09-41f0-adbc-f67784037ad6","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.988359Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d54717ddc911cb1f64f4a024df13195f96500367d36c4cd0f324eefa98cbf5a6","observation_id":"84352060-755e-43eb-af4c-0733432fe52c","resolution":{"observed_at":"2026-08-12T17:35:43.449126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.431403Z","title":"Unsupervised domain adaptation by backpropagation,","venue":null,"work_id":"3344c754-6bbc-4ad2-9df3-acedf981cf22","year":2015},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.992978Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:f7fdd1e4430a16213e13f060da6331505b2fd6d47ddefb7ea5fdfa230af86cd5","observation_id":"c16fdab8-cc72-4a8f-af08-51032ee74ffb","resolution":{"observed_at":"2026-08-12T17:35:43.435548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.419382Z","title":"Domain-adversarial training of neural networks,","venue":null,"work_id":"54aacfff-91c2-4015-85b6-a05bf52730fa","year":2016},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:41.996773Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e72f2b9d7792f3b230f3474760153ef57a4b0d0324dd280b2ef2c3d413d3b58d","observation_id":"80cecf7c-435c-40bb-b1e8-642dbcb78283","resolution":{"observed_at":"2026-08-12T17:35:43.423471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.405575Z","title":"Tuning pre-trained model via moment probing,","venue":null,"work_id":"5cbbee43-1c42-4538-9667-42366a91a4d2","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.000703Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7ad8007bed8722a9c83363551d167c2a318eb27068852c157a7f901cf84e4301","observation_id":"bd6f7065-447b-4008-a4d0-62810be3fa7e","resolution":{"observed_at":"2026-08-12T17:35:43.410527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.391367Z","title":"Generative adversarial networks,","venue":null,"work_id":"db24510e-a526-4f22-b946-9d63bfcad4e2","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.005213Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c414cf1dee532cfff41f2c26e69d30bfc2de4d71db7f7a045f57b486da15624f","observation_id":"3b517945-0b30-413a-9714-b6e054587cb1","resolution":{"observed_at":"2026-08-12T17:35:43.395872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.378030Z","title":"Self-supervised relationship probing,","venue":null,"work_id":"e6962c60-3b57-44fb-9c5a-654a1a77169a","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.009222Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:45aefaeefbdfd10636d9c4880e3160ab8c3dfe84eb800eba4e2b341a466c59db","observation_id":"6afc9006-0fc4-4356-b198-042c3bcb662c","resolution":{"observed_at":"2026-08-12T17:35:43.382535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.365246Z","title":"Sigma: secure gpt inference with function secret sharing,","venue":null,"work_id":"9af965f9-dac1-4cae-b6d9-f738131e8583","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.013035Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d7251363a00a2693991d53c5c286bc8800bbd56d70a3300384726afb60df6ed5","observation_id":"8afff277-4d76-493e-83e3-d8469abd0fef","resolution":{"observed_at":"2026-08-12T17:35:43.369960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.353232Z","title":"A survey on vision transformer,","venue":null,"work_id":"462188f2-7e9f-4c62-8015-bed12f70eb47","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.017450Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b8d9c57d18250a13699c361046617cd94a7abcbb10fb4b2c736ffd785352af7a","observation_id":"845721a4-08c6-4d91-b3f3-4500f2bd5cab","resolution":{"observed_at":"2026-08-12T17:35:43.357519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.341457Z","title":"Pre-trained models: Past, present and future,","venue":null,"work_id":"885dfa7c-32a0-4925-997d-df58edf76476","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.022252Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:ce9f7b8d1b64ea8cc42d816f9264d1ca27cf7db0c58b8a8193d324895eab1471","observation_id":"92ca2f96-f4fd-4723-aee9-71a954fba958","resolution":{"observed_at":"2026-08-12T17:35:43.345476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.329245Z","title":"Masked autoencoders are scalable vision learners,","venue":null,"work_id":"93056e2a-1c78-4188-8743-9c7410b893c5","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.026377Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d6e032de0ff66ac46929a0b18617d7eedd1c7c5fa37228ccdf90d2d15d89c302","observation_id":"a513867b-7d6a-43ba-a9f6-5073fe77e25f","resolution":{"observed_at":"2026-08-12T17:35:43.333641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.315201Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":"ce1b7fa4-0362-41f6-b0ba-330c6e8d594b","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.030398Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:8a1bf8f75eb9a296190d33cb89fd239460f215d1636ed65c2cf104707dbac932","observation_id":"6f11f7a4-9967-4d7d-8b1b-371f73954e7e","resolution":{"observed_at":"2026-08-12T17:35:43.319692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.299977Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"2950c6c2-98ed-477f-9665-9a80197cf0eb","year":2016},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.034163Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:5cab3183fbe8c8010cd6b2d12fb550ddd52a262098b604d87256e849be5de8ad","observation_id":"bd335490-7d5b-442e-9310-0337a0590c4f","resolution":{"observed_at":"2026-08-12T17:35:43.306310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.286323Z","title":"Using self-supervised learning can improve model robustness and uncertainty,","venue":null,"work_id":"e4afcf52-6ae0-49be-8229-153d0465c35e","year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.038502Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:9f10e2bd2fb8dd1010203c5c1e4aafffeb7576f38fab41771825024fc27a8fd5","observation_id":"6c5312e8-37c9-4ef8-8581-83ddc983cfef","resolution":{"observed_at":"2026-08-12T17:35:43.291077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.272844Z","title":"imagenette","venue":null,"work_id":"4b572f4b-e85a-4b9e-bf56-f8c2d2147169","year":null},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.042419Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:5c2f2bd6534ec6c47e8d0ad7f7bd04de5705c1106e867d28aa73f1ec8f30c053","observation_id":"29793f2c-5c5c-4ff4-a3fa-42ca80ca4730","resolution":{"observed_at":"2026-08-12T17:35:43.276899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.260498Z","title":"Fastai: A layered api for deep learning,","venue":null,"work_id":"47fd1614-e715-40c1-af73-5f4f1f97b874","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.047134Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:f209fccebb11c86d05cc7da51f7d29c80c676eb107378dc8d54331ed0a7f5963","observation_id":"e0680ce1-a53b-415a-b003-5e786f19db9d","resolution":{"observed_at":"2026-08-12T17:35:43.264754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.247267Z","title":"A database for handwritten text recognition research,","venue":null,"work_id":"1c4e0c47-f625-4d5a-9397-4c48ac9467d9","year":1994},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.051693Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:26a87b5f6805efa8c6221bad616c480631b86ef6eb7ccc32ebe50078f3ffabcb","observation_id":"6871c85f-52cd-408c-930f-7e8685945395","resolution":{"observed_at":"2026-08-12T17:35:43.252065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.232596Z","title":"A review of deep transfer learning and recent advancements,","venue":null,"work_id":"e0b26737-b494-42dd-ab26-43fb5d4acbdc","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.056154Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:86f4d673d88f8b1b6f1071cfa1e65ff005617fac999b81a1eec8b4bfa65848b3","observation_id":"d6baa4f4-dc59-4be1-92bf-aededc263840","resolution":{"observed_at":"2026-08-12T17:35:43.237548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.214976Z","title":"Gender and ai: Addressing bias in artifi- cial intelligence,","venue":null,"work_id":"96acf1f0-f883-4f20-a432-1173f44118a3","year":2024},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.060135Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:a89806724d71f3818e7395b6afee15a0b2854b084aef7c9fe257c694993fd100","observation_id":"4ed1f7da-f0cd-4a11-b71e-e22c479aa2f0","resolution":{"observed_at":"2026-08-12T17:35:43.221042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.063945Z","title":"A survey on contrastive self-supervised learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.063945Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:091f7a82040e9d20e93c9f492d1e783960728ea0bd9b647dc78c9b134598f290","observation_id":"5db1c377-5b42-405b-b525-46940c7df153","resolution":{"observed_at":"2026-08-12T17:35:42.063945Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.190120Z","title":"Entangled watermarks as a defense against model extraction,","venue":null,"work_id":"1bcea635-267a-4e31-9ac9-350aacdf8ece","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.068175Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:f2643885d21240e8d822bb3f8161ee9e4942d7d40995b47af69ff3ae1def4a27","observation_id":"349e0ee6-2ea2-4a14-a89a-f7213e9dbd20","resolution":{"observed_at":"2026-08-12T17:35:43.195092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.176707Z","title":"Visual prompt tuning,","venue":null,"work_id":"931ffa5f-33ad-4d16-9131-261be3a0b6e2","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.072674Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:647e1cae3f5edd0fb15aa244e009819a2b3ce644f039f3f37b5e4cdb8154deb0","observation_id":"314dfed5-d34d-4820-af1b-d9843d0a71f5","resolution":{"observed_at":"2026-08-12T17:35:43.181521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.076834Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.076834Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:1d3b9169c5f0fdd8e4a6ec3d05c7d68f2167deca518f8f1d48695e5f1cbfc942","observation_id":"9972938e-930f-4ae3-93b7-4c988843f905","resolution":{"observed_at":"2026-08-12T17:35:42.076834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-12T17:35:42.081506Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.081506Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:50da4b6364dcd4fdee255ea749441ce36df3469bd5aa57515c1ef67688cb8bd4","observation_id":"10ad5c56-b78a-4ce0-9ee6-e20322b3a0d5","resolution":{"observed_at":"2026-08-12T17:35:42.081506Z","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":"2026-08-12T17:35:42.085783Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.085783Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:876bfda0ceb84b11d1cb38689cbfa5fa04ac2342cac3c7591c2ffe5cc7cd66d4","observation_id":"c9bffe2e-f954-4924-b166-db7c38a86a3d","resolution":{"observed_at":"2026-08-12T17:35:42.085783Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.146053Z","title":"Contrastive representation learning: A framework and review,","venue":null,"work_id":"9fa2148f-dcb0-438b-a53c-0961546d8cf8","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.090076Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e05e777d9efa65105e9af3d636c714dd06371bc8d6dda7ffa3ca8eea056cdb5c","observation_id":"2070d0f8-94f5-45a3-919b-4907d071b64a","resolution":{"observed_at":"2026-08-12T17:35:43.150616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.133795Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":"53ace42f-c1d6-4a9f-b5ab-7ee42cb73855","year":1998},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.094935Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:84c68ec206f8ef9fd03dc83031136324fa5fd6bbc24ea9148c336e6644482548","observation_id":"a34619d1-c79d-4a43-b408-bda7860ee779","resolution":{"observed_at":"2026-08-12T17:35:43.138100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.120806Z","title":"Modeldiff: Testing-based dnn similarity comparison for model reuse detection,","venue":null,"work_id":"1cc236a5-9968-4d21-9b51-aedc8edf9d5e","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.099187Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b217898ac13d5a54a0d0abce4ba084ef0716799a35f5772e776db9a462e63771","observation_id":"783228bf-9505-49de-804f-1c2b407748be","resolution":{"observed_at":"2026-08-12T17:35:43.125438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.106084Z","title":"Transtailor: Pruning the pre-trained model for improved transfer learning,","venue":null,"work_id":"245c9288-f5e5-4ffd-9c88-11302a386e10","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.103582Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:3e9f8c9e455a89d2b0d5f4708da440aa8384a009d2c295ec66939e01214d26d1","observation_id":"00852f53-833e-4353-9ea9-d4771ad29e67","resolution":{"observed_at":"2026-08-12T17:35:43.110907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.093075Z","title":"Secdeep: Secure and performant on-device deep learning inference framework for mobile and iot devices,","venue":null,"work_id":"892d62f2-5a89-4eb1-9fc4-969715f666df","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.110705Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:0bae769ee817f1418158b77cca8822e4fb2cb28dab6f6f874e8a1c2672aa4722","observation_id":"2d504c4b-7b82-48d9-87fb-e21b0ca5d033","resolution":{"observed_at":"2026-08-12T17:35:43.097355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.080702Z","title":"Fault injection attack on deep neural network,","venue":null,"work_id":"08792c2e-2fc0-4c4b-80d9-203cf858d9dc","year":2017},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.115148Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:68faefebf51dcd8f0b5ec17e78a21d713adaa4a1226f9178981e40482d74054d","observation_id":"728fd2eb-aa2a-4a83-81c1-992b5bcfe74d","resolution":{"observed_at":"2026-08-12T17:35:43.085084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05270","last_updated":"2019-03-05T05:58:11Z","snapshot_observed_at":"2026-08-10T10:06:07.580743Z","submitted_at":"2018-10-11T22:15:28Z","title":"Rethinking the Value of Network Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05270","snapshot_observed_at":"2026-08-12T17:35:42.119434Z","title":"Rethinking the value of network pruning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.119434Z"},"links":{"cited_paper":"/paper/1810.05270","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:4297ef4902a7144ffc6423b85f4d189b06de8ed81adac34c5c4238d43bfa90ea","observation_id":"8b2229ce-1063-4b7d-a530-a94902444101","resolution":{"observed_at":"2026-08-12T17:35:42.119434Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.067686Z","title":"Transfer learning from pre-trained models,","venue":null,"work_id":"37d31e78-7381-46ae-af90-0cb28a9433d5","year":2018},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.123631Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:489d62cbca49808d74a37047b9dc35ec8f5c8eb64b4b10adca7917f22cba37d0","observation_id":"38485150-b20c-46f1-8b1a-366540cbeada","resolution":{"observed_at":"2026-08-12T17:35:43.072081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.053426Z","title":"Is artificial intelligence dangerous? 6 ai risks everyone should know about,","venue":null,"work_id":"fb940435-01f0-4437-bc6e-1280df3f12e4","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.127603Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7a66fc7ce64c9c3e30f7ff0dadf53752269af00194a97556f85f7ab3c9445d11","observation_id":"b7ac661c-f44d-4aa5-8f2f-8ebb586b6d4a","resolution":{"observed_at":"2026-08-12T17:35:43.058045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.039386Z","title":"Data augmentation for improving deep learning in image classification problem,","venue":null,"work_id":"4bfbc29a-21c8-447b-8ca6-d132ffe42781","year":2018},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.132287Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:508eebedf6725cce18cb7f9c1fe131b51c8583484d236ed7bd8bb8565f758c55","observation_id":"c8bf48aa-45cf-48f6-8071-da62586e4ad1","resolution":{"observed_at":"2026-08-12T17:35:43.044599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.026710Z","title":"Reading digits in natural images with unsupervised feature learning,","venue":null,"work_id":"f8d2991e-40cf-491c-aa79-121fb835de47","year":2011},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.136450Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d32c875e93d80216fea23d43faafe5adff4be4c52d483c23a0116163a0483fb9","observation_id":"0b403ba9-f57d-4d6f-a714-debc2ef325ae","resolution":{"observed_at":"2026-08-12T17:35:43.030993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.013937Z","title":"Openai’s embeddings api,","venue":null,"work_id":"c5247110-6a02-4e17-9f32-ad8ba0c5fc0b","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.140877Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:59ef835ee7cd1aee1b8d1afe9ca2f45e4134ebe99e40924e7b5ce0850b7ca850","observation_id":"ebdfba45-3e5d-431b-af9c-659d46f8b127","resolution":{"observed_at":"2026-08-12T17:35:43.018381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:43.000717Z","title":"The unsurprising effectiveness of pre-trained vision models for control,","venue":null,"work_id":"42d7e30e-fb0d-4149-aa9f-b0f4dff0aeaf","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.145177Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b5e22e2127f62d58293a9d499362d499d7edcfbcbdd7ddbefe296d78000032c9","observation_id":"47ede02e-de8c-4078-a0d2-60bcf64d5892","resolution":{"observed_at":"2026-08-12T17:35:43.005398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.986721Z","title":"Llm self defense: By self examination, llms know they are being tricked,","venue":null,"work_id":"52909145-94e3-4088-9799-0c6e55eb94b9","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.149494Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e6b4c3de293b5b7174db2f016065b7bb2f6e9e681b637cfc4c71117855951f34","observation_id":"10033df4-e5b8-434d-b8b8-7a627f0c0acc","resolution":{"observed_at":"2026-08-12T17:35:42.991333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.153451Z","title":"Early stopping-but when?","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.153451Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:3ad5b08107991109ff9610eaba16b3971f5d523bad1dddc3852804a3090bd47e","observation_id":"01c5419f-9222-477f-b4ef-ec1fe5b36eac","resolution":{"observed_at":"2026-08-12T17:35:42.153451Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.964873Z","title":"Pre-trained models for natural language processing: A survey,","venue":null,"work_id":"61bbf3c9-a3fa-4f29-9af6-99821043e16a","year":2020},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.157295Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:758a1741e6b7dad0c59259182eacbe535c5370577c195e53fa1dc776421cbf4b","observation_id":"9259669f-ef3d-43cc-a2eb-7638f3b5df88","resolution":{"observed_at":"2026-08-12T17:35:42.969178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.951965Z","title":"Reaas: Enabling adversarially robust downstream classifiers via robust encoder as a service,","venue":null,"work_id":"17584508-7b78-4bbb-95f4-e1e311dd7af0","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.161205Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e592b77a3456ce58692cee1696ee9d05e43b79b2604a989130f018579b10f4a2","observation_id":"ba9d2d7a-e874-4af6-a468-f14423c7f392","resolution":{"observed_at":"2026-08-12T17:35:42.956283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.936642Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":"9d7979ec-97d4-4795-8d41-7484b3eca9f4","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.165565Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:d191a516c4e6857d2cc89adb8b07a0d4ab15e3b8ecac997e61740d20a02c61df","observation_id":"a62aed8a-a424-4c02-a84f-3dc408f404cd","resolution":{"observed_at":"2026-08-12T17:35:42.943073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.923605Z","title":"Bit-flip attack: Crushing neural network with progressive bit search,","venue":null,"work_id":"b86dc82d-748f-4e7e-96d7-4aa1c9b61556","year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.169362Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:6c999a427c8d717952b4c5bde31986b5e599d8d4cbc19fbecb21d2489fce7e5d","observation_id":"74e9e0a3-af7c-4663-818c-a67fe6cb1bc7","resolution":{"observed_at":"2026-08-12T17:35:42.927989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00719","last_updated":"2021-03-07T18:38:24Z","snapshot_observed_at":"2026-08-09T08:11:37.373377Z","submitted_at":"2020-05-02T06:19:20Z","title":"Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00719","snapshot_observed_at":"2026-08-12T17:35:42.173423Z","title":"Probing the probing paradigm: Does probing accuracy entail task relevance?","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.173423Z"},"links":{"cited_paper":"/paper/2005.00719","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:3c2bcf3a21d489d9d3cbc102b452c51b11115503c870d05a140cbb83964c1013","observation_id":"68c17f12-b4b0-4b5b-878e-691188e64116","resolution":{"observed_at":"2026-08-12T17:35:42.173423Z","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":"2026-08-12T17:35:42.177649Z","title":"Gaussian mixture models","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.177649Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:a918f567fee1f83b2780fe5fc263ee46d2b78a99a35384827631ee17ccc886b1","observation_id":"31ba5976-6c26-4a22-9ffa-a999d0851bbd","resolution":{"observed_at":"2026-08-12T17:35:42.177649Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.902133Z","title":"High-resolution image synthesis with latent diffusion models,","venue":null,"work_id":"4a56f9f3-bf97-4dba-9cf8-c1c1ce073543","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.181961Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:e0ece7f35ab733f4eda4c031880333b566c37448761f4056f8d84dd11044ac84","observation_id":"58bb3853-fb3d-49d6-8195-f568ea954d1c","resolution":{"observed_at":"2026-08-12T17:35:42.906670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.888853Z","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization,","venue":null,"work_id":"82235ea4-c869-4960-b5f9-6abfae338eb9","year":2017},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.185863Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:a4e7a3278f50e51918ef2055c8fdfc8aa283c39a1d316648598af0c74d68f215","observation_id":"a1ba2eee-ab53-44a5-8468-6c06ac41da98","resolution":{"observed_at":"2026-08-12T17:35:42.893656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.874795Z","title":"Financial feature embedding with knowledge representation learning for financial statement fraud detection,","venue":null,"work_id":"6f34cf96-cf35-4df6-9039-f57e70eabd7b","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.189699Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:739519181655de70411b64f3be41df126411937a1fc025a7679e17448d6079e0","observation_id":"bad51658-7aa0-4815-9b7f-b9272e18a3a6","resolution":{"observed_at":"2026-08-12T17:35:42.879552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.857450Z","title":"A survey on image data augmen- tation for deep learning,","venue":null,"work_id":"275e6883-e503-4d29-86bc-f64302620a00","year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.193824Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:1c38b093cebdfd5b8e298454df5c0f34011fc20dd34840afaf6e91ce42276995","observation_id":"77f48402-b101-4d45-b45c-61fe2bd93795","resolution":{"observed_at":"2026-08-12T17:35:42.863288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.843002Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":"43626059-e983-40d3-8f20-4a025363d45c","year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.197673Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:ce7cf2053ef899bb25f80c911bd394b70317683c14197b36e273b4db5e8c3fde","observation_id":"89ca4128-7e95-42e3-84f9-954c24d9f6cf","resolution":{"observed_at":"2026-08-12T17:35:42.847849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.827623Z","title":"Convolutional neural networks for medical image analysis: Full training or fine tuning?","venue":null,"work_id":"30714bf8-0565-46be-a874-f9ed2987f259","year":2016},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.201773Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:2514fb02d8a2e9ce3d5e1c25b2cc38315c536faa88adbed36ae702f54dd0c804","observation_id":"56fecc20-210f-4c98-8dbe-74adf6153134","resolution":{"observed_at":"2026-08-12T17:35:42.832972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.812521Z","title":"Federated learning from pre-trained models: A contrastive learning approach,","venue":null,"work_id":"a3aef83b-b553-4435-be2e-464dfb553edf","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.205627Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7c9ddd55e6f4a63c9827f802717f14a2b9bf6bc574c74b6e5cc0b0caa3a6da67","observation_id":"f8cbc695-b38f-4307-8808-606cceff6b03","resolution":{"observed_at":"2026-08-12T17:35:42.817226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.796954Z","title":"Ai bill of rights: Algorithmic discrimination protections,","venue":null,"work_id":"95d9f73f-218a-4e58-863f-01e2016f0534","year":2024},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.210177Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:cf394564941046e3310ff3bfd24441b678fdecd25e26ffea40896d9c976359ca","observation_id":"f73b324a-dea4-4a71-be84-5b86ef2274bf","resolution":{"observed_at":"2026-08-12T17:35:42.802846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.214145Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.214145Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b7cce36240e162f7df3fec332ce50beefe8364dcc9a1b6229628c6547ce9bb1a","observation_id":"e9266638-dc09-4a26-9ffe-1bf8aa813da8","resolution":{"observed_at":"2026-08-12T17:35:42.214145Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.769928Z","title":"Pre-trained language models and their applications,","venue":null,"work_id":"3f9f09dd-d90b-43ef-9095-531d258e4318","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.218501Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:fb5588774aa06fd6861daf048ac548f033d588a6c1be2a94c336e4c7e21bc0b4","observation_id":"efbb7529-e495-4a2f-b51e-6e16c9337519","resolution":{"observed_at":"2026-08-12T17:35:42.774468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.754047Z","title":"Model barrier: A compact un- transferable isolation domain for model intellectual property protection,","venue":null,"work_id":"2bff450c-2525-4c6a-896b-21929835e053","year":2023},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.222669Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c5f520160f31ab73f17acc5c71ff3c9014e771f9a5413b1cec2c10595f7da2fe","observation_id":"a5701998-7dfc-4ec3-ad16-9f71783c5c2e","resolution":{"observed_at":"2026-08-12T17:35:42.761791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.737634Z","title":"Non-transferable learning: A new approach for model ownership verification and applicability authorization,","venue":null,"work_id":"1d5153eb-b106-4aa1-b03d-8c1553427fac","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.226882Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:b9b548be1a76e9b4d11cd70529173d88cadf61cba2e8f8f3c1a1754860b48c32","observation_id":"cbf042a2-5ab1-4911-a767-d3ca2009bf3f","resolution":{"observed_at":"2026-08-12T17:35:42.742558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12390","last_updated":"2022-05-24T22:44:43Z","snapshot_observed_at":"2026-08-13T15:37:43.100326Z","submitted_at":"2022-05-24T22:44:43Z","title":"Toxicity Detection with Generative Prompt-based Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12390","snapshot_observed_at":"2026-08-12T17:35:42.231918Z","title":"Toxicity detection with generative prompt- based inference,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.231918Z"},"links":{"cited_paper":"/paper/2205.12390","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:452093e206d5580855cb4511c07f88abd2124b8ee1911fb8ee36a96d368efdc0","observation_id":"75082e68-0408-4cc1-976d-4d818878c097","resolution":{"observed_at":"2026-08-12T17:35:42.231918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.10185","last_updated":"2021-05-21T07:53:10Z","snapshot_observed_at":"2026-08-14T07:14:01.944351Z","submitted_at":"2021-05-21T07:53:10Z","title":"A Non-Linear Structural Probe","version":1},"cited_work":{"arxiv_id":"2105.10185","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.10185","snapshot_observed_at":"2026-08-12T17:35:42.386424Z","title":"A Non-Linear Structural Probe","venue":"cs.CL","work_id":"8d91c671-f61c-464c-b627-43ab87519880","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.236469Z"},"links":{"cited_paper":"/paper/2105.10185","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:acbfbd965a0c51d3ccb96d5c2846f7029c60c103b863d0b64a1284e412b32dc8","observation_id":"7e684358-cc80-4f54-8e98-9937630194b1","resolution":{"observed_at":"2026-08-12T17:35:42.394622Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.724013Z","title":"Structured model probing: Empowering efficient transfer learning by structured regularization,","venue":null,"work_id":"52277032-35e4-437e-bf61-2e54753cc1b6","year":2024},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.241043Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:7b98156438585b688f2894fd97dd7d5a635793e89741d792dd15f4f6423343b6","observation_id":"d0c8a56a-4039-4ebe-87b3-3764b215bc33","resolution":{"observed_at":"2026-08-12T17:35:42.728666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.710356Z","title":"Fine-grained visual prompting,","venue":null,"work_id":"9353ef86-6c85-4631-8634-7d270269eb79","year":2024},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.245986Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:87a1629e154fef287f13471503b7879e4bdff18e23b990d0f3f7e3184b099bc3","observation_id":"777ad74d-77d9-463a-8578-e27ddda0a62b","resolution":{"observed_at":"2026-08-12T17:35:42.715031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.695195Z","title":"Robust watermarking for deep neural networks via bi-level optimization,","venue":null,"work_id":"a8f1f339-36df-4383-9a13-eedbabf2f6a4","year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.251564Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:0830c48ce6f0933b2db477966aecb433a0d93725a70e6fd13b4536a2d26d6eb6","observation_id":"65138e8d-ac78-4726-94d7-cc9daec66cf2","resolution":{"observed_at":"2026-08-12T17:35:42.700579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.679233Z","title":"Graph representation learning in bioinformatics: trends, methods and applications,","venue":null,"work_id":"8fd92ab5-0306-4a50-90a5-250897fe8235","year":2022},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.256461Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:754d5fa76392ecc2694c5757247725cdb01fe24cdfeb7c21682d902874e0276e","observation_id":"0a18c4ce-bfd7-46c2-ba57-1551b4f1622b","resolution":{"observed_at":"2026-08-12T17:35:42.685012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11432","last_updated":"2021-11-22T18:59:55Z","snapshot_observed_at":"2026-07-06T12:11:02.119174Z","submitted_at":"2021-11-22T18:59:55Z","title":"Florence: A New Foundation Model for Computer Vision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.11432","snapshot_observed_at":"2026-08-12T17:35:42.261300Z","title":"Florence: A new foundation model for computer vision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.261300Z"},"links":{"cited_paper":"/paper/2111.11432","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:cf1bc32f4632c7b4f501a108725e9c5f789ccee6c70da458af2b8e2556df717e","observation_id":"42f8e3cf-6958-4138-9c3e-83495b3269b7","resolution":{"observed_at":"2026-08-12T17:35:42.261300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1212.5701","last_updated":"2012-12-22T15:46:49Z","snapshot_observed_at":"2026-08-14T09:22:38.610170Z","submitted_at":"2012-12-22T15:46:49Z","title":"ADADELTA: An Adaptive Learning Rate Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1212.5701","snapshot_observed_at":"2026-08-12T17:35:42.265839Z","title":"Adadelta: an adaptive learning rate method,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.265839Z"},"links":{"cited_paper":"/paper/1212.5701","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:8869c68c1dc3bc06fc6e01bbfdbb96cb2d880c5a0b2c76ad282b5e36ab675595","observation_id":"9f507451-4884-43ea-8244-fc2c8069a887","resolution":{"observed_at":"2026-08-12T17:35:42.265839Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.665781Z","title":"Protecting intellectual property of deep neural networks with watermarking,","venue":null,"work_id":"e1a0fd82-eaa4-4afd-b29a-2aa5525871be","year":2018},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.269970Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:143b4e88fa4687a80625f3b1a5de45dfccd76eddf07860c40ab7c76a51185d33","observation_id":"cd27c0e0-43b5-4477-8ef2-7eb286860de4","resolution":{"observed_at":"2026-08-12T17:35:42.670093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.652870Z","title":"Fault sneaking attack: A stealthy framework for misleading deep neural networks,","venue":null,"work_id":"46ffaea3-5680-4b58-aba9-a56cfe194b49","year":2019},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.274289Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:c313f1b572c6358de51a9f1689a199597a7ec67ec4049312dfd2920447193011","observation_id":"cb8df323-3538-4f4b-ac31-54f024d476a5","resolution":{"observed_at":"2026-08-12T17:35:42.657476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.638534Z","title":"An overview on data representation learning: From traditional feature learning to recent deep learning,","venue":null,"work_id":"92f95860-6821-4972-8f43-664d3ede8e8d","year":2016},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.278381Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:634974985ec2d77f223ae7c54ab7cd098acc000aa13c57d3b94881676f03ba0e","observation_id":"9ff9d40f-2064-4b0f-a5fe-80877c823d4a","resolution":{"observed_at":"2026-08-12T17:35:42.643561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.624627Z","title":"Archlock: Locking dnn transferability at the architecture level with a zero-cost binary predictor,","venue":null,"work_id":"d44904fb-f72c-4160-9269-6bf0784071f7","year":2024},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.283392Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:22e308792e2d0ce9a97e13b82fd14cb0d36573dd1713444e35dcd6ef95cf3562","observation_id":"9c5d78bb-5670-42ed-b95a-80bf78910565","resolution":{"observed_at":"2026-08-12T17:35:42.629441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.01878","last_updated":"2017-11-13T18:40:16Z","snapshot_observed_at":"2026-08-05T00:54:50.482913Z","submitted_at":"2017-10-05T04:26:49Z","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.01878","snapshot_observed_at":"2026-08-12T17:35:42.288017Z","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.288017Z"},"links":{"cited_paper":"/paper/1710.01878","citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:63cd5d382ca83c50de0c254460b102dd64a8500c21807ad04f854dc42640caf5","observation_id":"7188df1e-1f6f-4df4-b41d-49a7ab5445cd","resolution":{"observed_at":"2026-08-12T17:35:42.288017Z","resolver_source":null,"status":"malformed_identifier"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:35:42.611880Z","title":"train- from-scratch","venue":null,"work_id":"f876d23a-ae0b-4d2c-8966-fb3623d743aa","year":null},"citing_paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T17:35:42.293715Z"},"links":{"citing_paper":"/paper/2411.12508"},"observation_digest":"sha256:ee60ce94d3e31843c95fc45345cb52a4bc7291074c7e1e7b0385ad6e0ddfa2cc","observation_id":"b4f6ebac-b55c-4f8c-bc60-61886f854cd1","resolution":{"observed_at":"2026-08-12T17:35:42.616138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.12508","last_updated":"2024-11-19T13:50:08Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-14T09:23:34.907192Z","submitted_at":"2024-11-19T13:50:08Z","title":"Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing"},"reference_resolution":{"displayed":93,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":2,"verified_fuzzy":59},"total_outbound_references":93},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 0 inbound Pith citation observations for arXiv:2411.12508."}