{"as_of":"2026-08-17T15:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c41740f70c634d3ad01721bc7cca1cc3f3b0a80b8da1f03674f2eacfc68ecf8d","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:28:00.772011Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2411.11525/citation-record","integrity":"/paper/2411.11525/integrity","json":"/paper/2411.11525/citation-record.json","paper":"/paper/2411.11525"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.333414Z","title":"Entropy-sgd: Biasing gradient descent into wide valleys","venue":null,"work_id":"0a5fd852-f934-4cd4-98d7-1aa2235a3554","year":2019},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.585467Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:53bf63019b33f154909fac8add20af77cdfb911c52daa5e7dd7dd07519bda899","observation_id":"c8acf341-21a7-4425-a320-9d87ef13497f","resolution":{"observed_at":"2026-08-12T18:28:01.336965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.03728","last_updated":"2018-11-09T01:08:00Z","snapshot_observed_at":"2026-08-16T05:42:20.700873Z","submitted_at":"2018-11-09T01:08:00Z","title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.03728","snapshot_observed_at":"2026-08-12T18:28:00.589638Z","title":"Detecting backdoor attacks on deep neural networks by activation clustering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.589638Z"},"links":{"cited_paper":"/paper/1811.03728","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:1102e82a5871a3a5143a5ce901896c62fd9a8dae7ca796b66ed6022a3536c81a","observation_id":"a6a17c65-977b-40f8-8051-446e005edcbc","resolution":{"observed_at":"2026-08-12T18:28:00.589638Z","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":"2026-08-12T18:28:01.320960Z","title":"Effective backdoor defense by exploiting sensitivity of poisoned sam- ples","venue":null,"work_id":"293e61d8-e1f1-4082-b62d-3a6b4142553b","year":2022},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.593811Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:58fb17ae919ebd2c6e88fa21b07029a5a8808a472d7366d758c1b0251be60119","observation_id":"8814c1c0-af44-4d19-9dcc-ca52366808f8","resolution":{"observed_at":"2026-08-12T18:28:01.325319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-12T18:28:00.597492Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.597492Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:d4ba0939e0818726e86e33e0280189caa8676e076621818680b79d01f00f5ffe","observation_id":"9a7ff392-3d8f-4b41-a61d-19cf4d150020","resolution":{"observed_at":"2026-08-12T18:28:00.597492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01548","last_updated":"2022-03-13T18:58:43Z","snapshot_observed_at":"2026-08-17T00:40:52.565251Z","submitted_at":"2021-06-03T02:08:03Z","title":"When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01548","snapshot_observed_at":"2026-08-12T18:28:00.602328Z","title":"When vision transformers outperform resnets without pre- training or strong data augmentations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.602328Z"},"links":{"cited_paper":"/paper/2106.01548","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:346b0328a10363291d0b5b2481b3ef20d7fec2cf71c68bad6ac9546fc4a1060b","observation_id":"38efff1b-a1f5-48c8-86de-3673e7c4ffe1","resolution":{"observed_at":"2026-08-12T18:28:00.602328Z","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":"2026-08-12T18:28:00.606409Z","title":"Certified adversarial robustness via randomized smoothing","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.606409Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:ac265cf9dd04796b128357249b8bc09ed7a4daf5bb9ae5288b076b07fd955728","observation_id":"737fe69e-c307-4044-b20f-e3c010df2e54","resolution":{"observed_at":"2026-08-12T18:28:00.606409Z","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":"2026-08-12T18:28:01.301476Z","title":"Lira: Learnable, imperceptible and robust backdoor attacks","venue":null,"work_id":"6fcdf79d-c2ef-4599-b04d-bc54f298d0d0","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.610054Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:92ca5e43d5fe785d2e17ee5a0f80c26903f946cc9d9f7cdd1b1372d0f7b36db4","observation_id":"de7fb254-084d-4299-8a6d-e130f0e9f9f0","resolution":{"observed_at":"2026-08-12T18:28:01.305332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.01412","last_updated":"2021-04-29T16:44:25Z","snapshot_observed_at":"2026-08-15T12:34:14.131778Z","submitted_at":"2020-10-03T19:02:10Z","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.01412","snapshot_observed_at":"2026-08-12T18:28:00.614002Z","title":"Sharpness-aware minimization for efficiently improving generalization","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.614002Z"},"links":{"cited_paper":"/paper/2010.01412","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:87104ad5fa60838de754a8c156cec130a69b92cc0b2be53156a8930f374a3f5f","observation_id":"e33f95d3-cfdd-4764-91a8-4f80efd615bd","resolution":{"observed_at":"2026-08-12T18:28:00.614002Z","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":"2026-08-12T18:28:01.290291Z","title":"Strip: A defence against trojan attacks on deep neural networks","venue":null,"work_id":"096f594a-917d-435a-900d-ad0c3b4c45b5","year":2019},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.618211Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:697cc31c86989d4b465ec4138f8cf7d6c6c5d55c9e4f6178848e7c1f6431b146","observation_id":"8f77cbc2-da52-4c17-8252-b07f142fa72b","resolution":{"observed_at":"2026-08-12T18:28:01.294148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.279269Z","title":"Badnets: Evaluating backdooring attacks on deep neu- ral networks","venue":null,"work_id":"04678d01-36f5-4fc9-9dc6-f3ab59615340","year":2019},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.621991Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:8e6160ba4193bf0940704fae99e761f7a3bdd8c220bf79c35417fd4fc1fbc69a","observation_id":"b7bd5007-d17c-49a1-84bf-90f3ca664e61","resolution":{"observed_at":"2026-08-12T18:28:01.283117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.268032Z","title":"Scale-up: An efficient black-box input-level backdoor detection via analyzing scaled predic- tion consistency","venue":null,"work_id":"1293c79d-d48a-4f7e-8dc0-6424bec51f6a","year":null},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.625802Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:35da0e32b1a5a6085cca24c1c3616c6e9e0f1379df54dd00e08cbcdbf68b6c4f","observation_id":"70aa0f17-d7c8-47c5-9f4b-6063add555c6","resolution":{"observed_at":"2026-08-12T18:28:01.272365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.256480Z","title":"Spectre: Defending against backdoor attacks us- ing robust statistics","venue":null,"work_id":"27a5ce5d-e022-4544-9b8e-28da9896e12d","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.629653Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:aa9d27363db8562c31cbc30d9b0a0bf2e1f33e576c24aa30f48e81daf60e9c10","observation_id":"08396100-405f-4d7f-b7d5-9266cdd30484","resolution":{"observed_at":"2026-08-12T18:28:01.260394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.633497Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.633497Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:9fc452d558c87b682ae2389ed7234c3b268e763ffbc97c725e7ea896ad0df963","observation_id":"793da2a4-0254-47e2-8b17-5970b7c4e4c5","resolution":{"observed_at":"2026-08-12T18:28:00.633497Z","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":"2026-08-12T18:28:01.236072Z","title":"Flat minima.Neu- ral computation, 9(1):1–42, 1997","venue":null,"work_id":"df11c059-152f-4780-aca5-9b4929ba4b01","year":1997},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.637742Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:00400908d3169cbd6fada1af0975b4686d5ef0a149cbb63f2e8bb794aef4a389","observation_id":"8b458ab4-18f6-43ce-9fe5-d259608a6aba","resolution":{"observed_at":"2026-08-12T18:28:01.240099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.641351Z","title":"Densely connected convolutional net- works","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.641351Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:be8a8b0d28f66052379dad8022836e177d641216eefba1a08596b633acb39466","observation_id":"4b1e9771-c1c1-4d71-8e2d-15059d787d60","resolution":{"observed_at":"2026-08-12T18:28:00.641351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10908","last_updated":"2023-09-13T06:11:12Z","snapshot_observed_at":"2026-08-16T16:00:05.739503Z","submitted_at":"2023-01-26T02:38:37Z","title":"Distilling Cognitive Backdoor Patterns within an Image","version":4},"cited_work":{"arxiv_id":"2301.10908","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.10908","snapshot_observed_at":"2026-08-12T18:28:00.896229Z","title":"Distilling Cognitive Backdoor Patterns within an Image","venue":"cs.LG","work_id":"d776062d-24c9-416d-b278-096c0a3e982b","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.644815Z"},"links":{"cited_paper":"/paper/2301.10908","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:753e2076b811bef215f59476ce035452767abb9e9e092814449f80bd15bed2ec","observation_id":"a211fe67-f009-4b49-8f70-8f4f4d074a1d","resolution":{"observed_at":"2026-08-12T18:28:00.902768Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.216206Z","title":"Backdoor defense via decoupling the training process","venue":null,"work_id":"e3fd5364-e1b0-4f01-a4e1-177d3c0ff025","year":null},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.648667Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:acb325c606860d3b64e11bf4f885faeae6d589a1abccbfbc84378b1be3808f82","observation_id":"a55594ac-e62e-4c80-af88-940686cdf0a8","resolution":{"observed_at":"2026-08-12T18:28:01.220410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.652437Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.652437Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:c9ef52ac39006ec5f5d9aa9d098c1999e8fe0c4f7f908385e5ef7ce3361cd51c","observation_id":"5c388cf6-b4d0-423b-9d0e-58cc0b5b8934","resolution":{"observed_at":"2026-08-12T18:28:00.652437Z","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":"2026-08-12T18:28:01.196147Z","title":"Asam: Adaptive sharpness-aware minimiza- tion for scale-invariant learning of deep neural networks","venue":null,"work_id":"7724f0c0-434c-4ba2-bd42-7cf6bdcc6f62","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.656654Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:ce6ccdd60e1074cefc45667f89b6f1e8b83976b9ca513edf60f32045292ef309","observation_id":"93f48b33-49f7-45fa-8e0a-a18169489e6e","resolution":{"observed_at":"2026-08-12T18:28:01.200387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.185004Z","title":"Invisible backdoor attack with sample- specific triggers","venue":null,"work_id":"c23e87d4-f1bc-45e6-acb6-4728002d1649","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.660082Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:4348d552fb40837c2c0fe6028539f61ec8dfc63b51db7e95a9a853a35b59cd1f","observation_id":"a131dbcd-53c8-488b-8392-68afce8420a1","resolution":{"observed_at":"2026-08-12T18:28:01.188941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.173706Z","title":"Anti-backdoor learning: Training clean models on poisoned data","venue":null,"work_id":"e51c53ee-7549-4d02-bde9-96f6d7324d24","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.663889Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:756589d1dcaf8d0a3a4fe52f0c23ef2edfd77833fb289655df6487bf35a3892c","observation_id":"c32c949d-92bd-4f45-936d-ca92bee18ff0","resolution":{"observed_at":"2026-08-12T18:28:01.178058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.162740Z","title":"Reconstructive neuron prun- ing for backdoor defense","venue":null,"work_id":"d2f3671c-ad32-4b33-ad71-f71c90b8af3a","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.667563Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:e1bb363c01001cba0f331ae72a5c15f78b48a5d3beb177341acbe13e629c1638","observation_id":"6a927f83-cd41-4143-92a6-c4940c54ca0e","resolution":{"observed_at":"2026-08-12T18:28:01.166693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.151382Z","title":"Badclip: Dual- embedding guided backdoor attack on multimodal con- trastive learning","venue":null,"work_id":"43e3cd29-29aa-421e-9082-dd70831065b3","year":2024},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.671202Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:67b2813d3cfbe4105e88229a2896559370e29c448f9eb77378f68a90318f961d","observation_id":"8715760a-0217-4c0c-b40f-7b6372769032","resolution":{"observed_at":"2026-08-12T18:28:01.154915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.140313Z","title":"Trojaning attack on neural networks","venue":null,"work_id":"36f49695-0abc-4650-a503-4ee9ea96d2c4","year":2018},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.674931Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:523123d614b0836e67e1c911909c5a651dd9cdca1a74a6c74ebdbb2b0d901d93","observation_id":"7c46ec45-8bad-4c6c-8c94-17d0c3c4428e","resolution":{"observed_at":"2026-08-12T18:28:01.144310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11715","last_updated":"2022-12-19T04:02:37Z","snapshot_observed_at":"2026-08-16T16:30:18.531844Z","submitted_at":"2022-09-23T16:47:19Z","title":"The \"Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11715","snapshot_observed_at":"2026-08-12T18:28:00.678626Z","title":"The” beatrix”resurrections: Ro- bust backdoor detection via gram matrices","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.678626Z"},"links":{"cited_paper":"/paper/2209.11715","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:27cf6a2506309efaeb2424da07cbd1599c19a9c0bf808970814d922c4f076379","observation_id":"a93493e6-16ae-422a-a49d-79ef3be89578","resolution":{"observed_at":"2026-08-12T18:28:00.678626Z","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":"2026-08-12T18:28:01.128230Z","title":"Wanet - impercepti- ble warping-based backdoor attack","venue":null,"work_id":"41ea8ded-bbf2-4492-9810-7c348a022759","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.682659Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:a1caedc5bb7c1998fcd546b6f372681acbbb70ee9f3b5145045932a3dddaeea1","observation_id":"254d88f3-1037-441f-a95e-7260a8e0b925","resolution":{"observed_at":"2026-08-12T18:28:01.132582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01730","last_updated":"2024-01-28T09:25:12Z","snapshot_observed_at":"2026-08-17T05:21:20.498960Z","submitted_at":"2024-01-28T09:25:12Z","title":"Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01730","snapshot_observed_at":"2026-08-12T18:28:00.686526Z","title":"Evaluating llm–generated multimodal diagnosis from medical images and symptom analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.686526Z"},"links":{"cited_paper":"/paper/2402.01730","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:625334a86b3c2e0f62d9ef4c9d4fa211395a68e0951caa1773eba737c61fa473","observation_id":"374eb4f0-2446-4f41-b73e-aa57c4cc7629","resolution":{"observed_at":"2026-08-12T18:28:00.686526Z","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":"2026-08-12T18:28:01.117499Z","title":"Revisiting the assumption of latent sep- arability for backdoor defenses","venue":null,"work_id":"02447bb6-6c59-4594-ae6e-b0136ab39956","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.690275Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:adfbd45d735351190d4bd8fdec51da17036399eaf31f2a45f750e5ed6fa8207c","observation_id":"2d06d89d-6641-47f3-962a-acd4cb929aa9","resolution":{"observed_at":"2026-08-12T18:28:01.121018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.106811Z","title":"Silhouettes: a graphical aid to the inter- pretation and validation of cluster analysis","venue":null,"work_id":"1f5f32f5-1417-42ca-8fe4-277b6bb7818b","year":1987},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.694327Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:42394aa35339e1e67a8ce827a56d15cd44855071313d8f023039bbc6b14198fb","observation_id":"0811d4a1-2d20-447c-a356-9254c1bcbd80","resolution":{"observed_at":"2026-08-12T18:28:01.110366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.698085Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.698085Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:deeb740be0765a8436b8ca5b2ccc7b883510e8f69e17978241c0653b3c8c38a1","observation_id":"604ae2b7-375a-494a-8158-06ed58a5c90e","resolution":{"observed_at":"2026-08-12T18:28:00.698085Z","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":"2026-08-12T18:28:01.088794Z","title":"When llm meets hypergraph: A sociological analysis on personality via online social networks","venue":null,"work_id":"6042d60e-8acd-4b43-944b-6349f06b4394","year":2024},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.701689Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:843f68042e77064d8a51a1e1b1f241c0f98831acb6013457b10511dee0e4f5af","observation_id":"fadb5a83-16a6-4aac-94e9-061e803a6f22","resolution":{"observed_at":"2026-08-12T18:28:01.092702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-12T18:28:00.705415Z","title":"Very deep convo- lutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.705415Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:391036d8213502776f2bee282d0a3b0db5cabfaacbe37671e0285eadff657a05","observation_id":"26d32b29-c81c-409b-a40d-e687fa7dad45","resolution":{"observed_at":"2026-08-12T18:28:00.705415Z","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":"2026-08-12T18:28:01.077380Z","title":"Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch","venue":null,"work_id":"cfdaf527-e48d-4f4f-9924-5e197f9c4566","year":2022},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.709190Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:d5f08757b1fba10eab4cd57d6d4f87cbf046f03834a367eacbcff39261b4ecbf","observation_id":"035dc9a9-9bf4-4652-ada7-94a3902e3844","resolution":{"observed_at":"2026-08-12T18:28:01.081340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.712861Z","title":"The german traffic sign recognition bench- mark: a multi-class classification competition","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.712861Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:2de9437ed3cab049b6cb4304a28a20494c2dae26b1f65eaac3ad335bdf2784ee","observation_id":"a2e05a90-969d-4ace-a2dd-90b7d9234d68","resolution":{"observed_at":"2026-08-12T18:28:00.712861Z","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":"2026-08-12T18:28:01.059015Z","title":"Demon in the variant: Statistical analysis of {DNNs} for ro- bust backdoor contamination detection","venue":null,"work_id":"ef8850b1-614b-4d60-900c-ca6249cd6b9e","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.716493Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:5f6067e0e8d94a03464379d17b99b18f3a24a4ccf5484d683164261c81798330","observation_id":"1d780cfa-7a2a-4df7-8dd1-8dd959134ce4","resolution":{"observed_at":"2026-08-12T18:28:01.062871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.048226Z","title":"Spectral sig- natures in backdoor attacks","venue":null,"work_id":"395e0164-ee47-43c3-9ced-e2d81a5be217","year":2018},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.720512Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:cab1d36afbd7aace5ae1ac4f33f0754643c67c8ed7dc7d46cda1e174a575eb67","observation_id":"30a166f0-1c60-43d2-be74-83417848601e","resolution":{"observed_at":"2026-08-12T18:28:01.052018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02771","last_updated":"2019-12-06T23:16:45Z","snapshot_observed_at":"2026-08-10T18:11:31.253134Z","submitted_at":"2019-12-05T18:05:59Z","title":"Label-Consistent Backdoor Attacks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02771","snapshot_observed_at":"2026-08-12T18:28:00.724582Z","title":"Label-consistent backdoor attacks","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.724582Z"},"links":{"cited_paper":"/paper/1912.02771","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:4001c6abb3ae10eddad180ef65a0a0eeaf04b9c934ff51285badb2a7e6923605","observation_id":"191d6971-b2d6-4b7f-ba5c-3e9e0959c1cd","resolution":{"observed_at":"2026-08-12T18:28:00.724582Z","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":"2026-08-12T18:28:01.037087Z","title":"Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples","venue":null,"work_id":"2f430c22-869d-4104-ac16-57a5ced88c89","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.728543Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:ce265ed7985bce910efea8f9c35b96de56675cf11b6087a84073a6e115bf099a","observation_id":"48453bb3-384d-4346-9b9b-9013d9e50bd1","resolution":{"observed_at":"2026-08-12T18:28:01.040967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.026285Z","title":"Backdoor- bench: A comprehensive benchmark of backdoor learning","venue":null,"work_id":"e1c1a1de-059e-4fca-8e5c-a7c734c75067","year":2022},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.732114Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:5d315c59888c8f04c6b663b7057dfa1fed9901130806f88b81547d8b431e8811","observation_id":"c8a12ce8-0fc1-4949-850d-1156ec4c6591","resolution":{"observed_at":"2026-08-12T18:28:01.030013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08890","last_updated":"2023-12-13T15:42:55Z","snapshot_observed_at":"2026-08-16T14:35:04.792682Z","submitted_at":"2023-12-13T15:42:55Z","title":"Defenses in Adversarial Machine Learning: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08890","snapshot_observed_at":"2026-08-12T18:28:00.735855Z","title":"Defenses in adversarial machine learning: A survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.735855Z"},"links":{"cited_paper":"/paper/2312.08890","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:27603a529a210c193685e83a19ddf0462c1aa119d31a3fa40a38bd13601894af","observation_id":"2be40452-340a-4af1-b554-cd39f54a0112","resolution":{"observed_at":"2026-08-12T18:28:00.735855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15002","last_updated":"2024-08-12T02:46:06Z","snapshot_observed_at":"2026-07-06T17:21:01.918787Z","submitted_at":"2024-01-26T17:03:38Z","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15002","snapshot_observed_at":"2026-08-12T18:28:00.739588Z","title":"Backdoorbench: A comprehensive benchmark and analysis of backdoor learning.arXiv preprint arXiv:2401.15002, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.739588Z"},"links":{"cited_paper":"/paper/2401.15002","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:1ee9f2c6f2e82ee6f419ba5f30ded8c090c9b108b74a1f5667cb682975b02f20","observation_id":"945ac8f0-f681-4117-8543-5d5307f36993","resolution":{"observed_at":"2026-08-12T18:28:00.739588Z","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":"2026-08-12T18:28:01.014682Z","title":"Adversarial neuron prun- ing purifies backdoored deep models","venue":null,"work_id":"c56433ba-f32e-4408-9290-357eb965769b","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.743493Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:45879199e651a6a2d49a76adffc27913fff6ab12f13b33f9ba2d59518093600c","observation_id":"a826c0b0-3f6c-49ec-92fa-18eeff8bd848","resolution":{"observed_at":"2026-08-12T18:28:01.018810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:01.003059Z","title":"Introspection of dnn-based perception functions in au- tomated driving systems: State-of-the-art and open research challenges","venue":null,"work_id":"cc97f2d9-22ce-484e-a56b-a9a082eb495c","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.746996Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:765a207f07a9889a7dfd593860563eb7ac0b80a5c6cbd22b5e0d87a06466a096","observation_id":"3ad35c22-a607-487b-81ac-49fd5eda5afd","resolution":{"observed_at":"2026-08-12T18:28:01.006867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06230","last_updated":"2024-05-28T03:36:40Z","snapshot_observed_at":"2026-08-16T14:35:57.720880Z","submitted_at":"2023-12-11T09:17:33Z","title":"Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06230","snapshot_observed_at":"2026-08-12T18:28:00.750435Z","title":"Activation gradient based poisoned sam- ple detection against backdoor attacks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.750435Z"},"links":{"cited_paper":"/paper/2312.06230","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:1e512ca754cd5f35f9ff8eb07d42ef060145daa63c5b03c3a8cfc12f59935be9","observation_id":"b58174a1-4b20-4949-acff-9adc6b4dd1f9","resolution":{"observed_at":"2026-08-12T18:28:00.750435Z","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":"2026-08-12T18:28:00.991185Z","title":"Rethink- ing the backdoor attacks’ triggers: A frequency perspective","venue":null,"work_id":"cc37398f-04db-455c-91a7-35ab71aab661","year":2021},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.754144Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:407f1b24cd63f3b24cb030cc3bd82039f5fc5158894e1f83ce648e8f2427ae63","observation_id":"78fa8ac0-b7ad-4185-bbd4-afafba7859a5","resolution":{"observed_at":"2026-08-12T18:28:00.995727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.979475Z","title":"Data- free backdoor removal based on channel lipschitzness","venue":null,"work_id":"1c803ef3-cee4-4da4-b5c8-1a4c689d99d3","year":2022},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.757424Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:f30b8da6a7e40cf805f2bc0f66d3145407db191ddb3e23f85c1cb14cfd8ab7f7","observation_id":"ede0ad55-d29a-49ba-a934-b74eb2abfaa1","resolution":{"observed_at":"2026-08-12T18:28:00.983280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.968373Z","title":"Enhancing fine-tuning based backdoor defense with sharpness-aware minimization","venue":null,"work_id":"79f12e07-38cc-47d8-9536-39ab23ec260c","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.760756Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:379818e0b5d404e29b45fe7af2af7c7bd0b3267907d00306528f312dbf7633b5","observation_id":"7f330b6d-07b6-4605-9f4a-92f2c6ccf398","resolution":{"observed_at":"2026-08-12T18:28:00.972163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T18:28:00.956806Z","title":"Neural polarizer: A lightweight and effective backdoor de- fense via purifying poisoned features","venue":null,"work_id":"f62365bd-c5c9-46aa-a836-10125bf8ada1","year":2023},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.764231Z"},"links":{"citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:d3f8f83950ab5eb383f5882833589c93e539a9b6f3d5cfc82e9b03ca094dce0d","observation_id":"1bf0445f-109c-4e5d-b2c0-94182cf4647a","resolution":{"observed_at":"2026-08-12T18:28:00.961130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16134","last_updated":"2024-05-30T04:45:11Z","snapshot_observed_at":"2026-08-16T13:49:31.362326Z","submitted_at":"2024-05-25T08:57:30Z","title":"Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16134","snapshot_observed_at":"2026-08-12T18:28:00.768438Z","title":"Breaking the false sense of security in backdoor defense through re- activation attack","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.768438Z"},"links":{"cited_paper":"/paper/2405.16134","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:a59e9747ab0439e9b439f51f2b8edd09aa8fb09066afa90987f5323647cc9153","observation_id":"eedfc425-9e4c-4582-a764-b125600950d9","resolution":{"observed_at":"2026-08-12T18:28:00.768438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.08065","last_updated":"2022-03-19T15:56:32Z","snapshot_observed_at":"2026-08-16T17:14:21.841080Z","submitted_at":"2022-03-15T16:57:59Z","title":"Surrogate Gap Minimization Improves Sharpness-Aware Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.08065","snapshot_observed_at":"2026-08-12T18:28:00.772011Z","title":"Surrogate gap minimization improves sharpness-aware training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:00.772011Z"},"links":{"cited_paper":"/paper/2203.08065","citing_paper":"/paper/2411.11525"},"observation_digest":"sha256:287151b9956c23a0e2d7b34d0e499f736ae7f0dd39c606da4d2867c7e7e0fce9","observation_id":"94bd5599-701d-4256-81db-8608f6d73c7b","resolution":{"observed_at":"2026-08-12T18:28:00.772011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.11525","last_updated":"2024-11-18T12:35:08Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T03:30:05.724904Z","submitted_at":"2024-11-18T12:35:08Z","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":30},"total_outbound_references":50},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2411.11525."}