{"as_of":"2026-08-14T08:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c828b9c430c2aba48c87653391439979f328a28aca1bb480b4610a9dd58291a9","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:27:16.751408Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T19:10:58.135925Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.05414","snapshot_observed_at":"2026-08-01T19:10:58.135925Z","title":"Liang, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17069","last_updated":"2026-07-19T04:21:10Z","snapshot_observed_at":"2026-08-13T07:16:37.120734Z","submitted_at":"2026-07-19T04:21:10Z","title":"AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T19:10:58.135925Z"},"links":{"cited_paper":"/paper/2508.05414","citing_paper":"/paper/2607.17069"},"observation_digest":"sha256:adb1e5f71d9d1c2875e2083ef8b309cb8f3d5a15674d54071a4e016e06fe118b","observation_id":"5d0cca62-612e-4431-a5a5-a44ae497be9a","resolution":{"observed_at":"2026-08-01T19:10:58.135925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.05414/citation-record","integrity":"/paper/2508.05414/integrity","json":"/paper/2508.05414/citation-record.json","paper":"/paper/2508.05414"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T23:27:17.087745Z","title":"End-to-end object detection with transformers","venue":null,"work_id":"277c3a17-1730-4bce-ae32-0ead0917ba5d","year":2020},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.640153Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:36897f38100d40a6b17a005d2bedab42266867873b35038680d759024c3d9f4f","observation_id":"46874ea2-f477-464c-a419-d25dd7287fd3","resolution":{"observed_at":"2026-08-05T23:27:17.090905Z","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":"2026-08-05T23:27:17.069669Z","title":"CARLA: An open urban driving simulator","venue":null,"work_id":"b479a1c3-9360-486d-ab7a-60d4ac0c013e","year":2017},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.650033Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:ea0d329aed36642160cc229ce8a967d41e4e37c011c01e043125b2bcff264e89","observation_id":"7f499a23-7ba8-4b6b-9ee8-999be54031ac","resolution":{"observed_at":"2026-08-05T23:27:17.072776Z","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":"2026-08-05T23:27:17.050572Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"d0ac7990-5591-471a-9c3f-0b181eb47f4e","year":2015},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.656227Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:0a8fe71b409e33b31b3e0fcce828767b2362030126f019729eff0be57a74c3fe","observation_id":"426d69d3-2f2e-4b67-9944-6f29066a9204","resolution":{"observed_at":"2026-08-05T23:27:17.054041Z","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":"2026-08-05T23:27:17.032931Z","title":"Fast r-cnn","venue":null,"work_id":"27ff7b12-5ffe-4a35-9ce2-9b57f2eec3cf","year":2015},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.666149Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:ba6b88fca3d3bd5111c5f04feaf5f7222c1504ed4692900c4ba39a2c660f341b","observation_id":"1930b9ff-e4ae-4617-9180-0165a72bb1d2","resolution":{"observed_at":"2026-08-05T23:27:17.035830Z","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":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-05T23:27:16.668924Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.668924Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:f9cd656b317e2faf3596cdd8a0b4bb9854800ab9e87dd48713656bc371720095","observation_id":"6cbe3095-c997-4899-81dd-33c3989ecc7a","resolution":{"observed_at":"2026-08-05T23:27:16.668924Z","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":"2026-08-05T23:27:17.016627Z","title":"Neural 3d mesh renderer","venue":null,"work_id":"c6d7491f-82b7-4dd0-96d0-223d4239355f","year":2018},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.674877Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:c9faf6ae295e4d3520f41513f262b1607b8e79b675d6e5bca56204c79837b9ff","observation_id":"98ce5f04-ab9a-4806-a785-018fd9f66c39","resolution":{"observed_at":"2026-08-05T23:27:17.019698Z","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":"2026-08-05T23:27:16.990615Z","title":"Generate more imperceptible adversarial ex- amples for object detection","venue":null,"work_id":"68313567-351f-412e-a336-f12ee51d045f","year":2021},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.686674Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:c8fdfdbc748b8917ba32475e9a18c4d6b83ba9ce29eade36066c7bbe4b080e40","observation_id":"0e2faf39-a14c-4e97-bea2-71fe4d5135b4","resolution":{"observed_at":"2026-08-05T23:27:16.994443Z","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":"2026-08-05T23:27:16.982428Z","title":"Exploring inconsis- tent knowledge distillation for object detection with data augmentation","venue":null,"work_id":"054989a6-377c-4dec-990d-76fdfbf4079f","year":2023},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.692661Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:c9e7e7c2cc26b9e756b07233ed37d7bd50803850dc2b08fc033f1303530b19a9","observation_id":"66087b96-00db-43db-a687-85191a510976","resolution":{"observed_at":"2026-08-05T23:27:16.985453Z","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":"2403.16271","last_updated":"2024-04-09T05:09:56Z","snapshot_observed_at":"2026-08-13T00:46:50.678581Z","submitted_at":"2024-03-24T19:32:39Z","title":"Object Detectors in the Open Environment: Challenges, Solutions, and Outlook","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16271","snapshot_observed_at":"2026-08-05T23:27:16.695316Z","title":"Object detectors in the open environ- ment: Challenges, solutions, and outlook","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.695316Z"},"links":{"cited_paper":"/paper/2403.16271","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:8b4d81fc5bb39768f905ec6f22d6270d67a59f7c68dd1c9c24515a7773249dc1","observation_id":"e1ee488e-c819-43e1-a151-64c3ec501102","resolution":{"observed_at":"2026-08-05T23:27:16.695316Z","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":"2026-08-05T23:27:16.973231Z","title":"Vl-trojan: Multimodal instruction backdoor attacks against autoregressive visual language models","venue":null,"work_id":"76fb90ae-1db6-4076-8012-0cfcafdfff49","year":2025},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.698650Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:81808dc9ab2a2a7e6c57301a266c0a9921e007252b13dc008cb62c57523d86da","observation_id":"22ba6fd3-6ffe-4ac4-8072-d56aa9ea1d24","resolution":{"observed_at":"2026-08-05T23:27:16.977129Z","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":"1804.02767","last_updated":"2018-04-08T22:27:57Z","snapshot_observed_at":"2026-08-06T11:09:16.409556Z","submitted_at":"2018-04-08T22:27:57Z","title":"YOLOv3: An Incremental Improvement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.02767","snapshot_observed_at":"2026-08-05T23:27:16.710778Z","title":"Adversarial instance attacks for interactions between hu- man and object","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.710778Z"},"links":{"cited_paper":"/paper/1804.02767","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:2d4006c8c722a6deb5665e5bd6ed1cf0cea0f7e7492dd9d40bb27d6d540f20ee","observation_id":"1eeed012-39bc-47de-85ad-8412005f2c3b","resolution":{"observed_at":"2026-08-05T23:27:16.710778Z","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":"2026-08-05T23:27:16.939808Z","title":"Faster r-cnn: Towards real-time ob- ject detection with region proposal networks","venue":null,"work_id":"b63d8120-2765-4090-8fe4-f87b5319aca7","year":2015},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.714611Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:68fdd91178c8340629afae95f071642149ec8022521842ab6ca68eb5fb10a58a","observation_id":"4591cc1d-a86f-4e6d-8fd9-440c83eae277","resolution":{"observed_at":"2026-08-05T23:27:16.942654Z","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":"2026-08-05T23:27:16.931320Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"302d2493-9857-4c73-877e-a93e22bf78ba","year":2016},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.717452Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:be14b2cf4bfd624aa8cfe18eb43d9be681aa9d6eccc74765ede32ad4a7365c3d","observation_id":"1339c8f2-9e94-4635-8e51-15a7be2da20b","resolution":{"observed_at":"2026-08-05T23:27:16.934166Z","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":"2026-08-05T23:27:16.923582Z","title":"DTA: Physical Camouflage Attacks Using Differentiable Transformation Network","venue":null,"work_id":"ac2db107-07d1-4582-b849-4cf969622bb6","year":2022},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.720584Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:d146a3cccd59b03afe2979bbc6cef3f4baf22c944c97b38bf0d9b674d32ccf8c","observation_id":"29760423-0645-4e96-b9b4-6dfdb09043dd","resolution":{"observed_at":"2026-08-05T23:27:16.926211Z","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":"2026-08-05T23:27:16.914275Z","title":"ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Ve- hicle Evasion","venue":null,"work_id":"281faf45-5ff6-4b55-bfdf-8d781127f67d","year":2023},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.724295Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:b2c3e0b80f397d68235eb3f0bd6c3f4df9eff340276dc151d62f41448dbbcbeb","observation_id":"08311e15-69bb-4a53-b546-6e8614489e81","resolution":{"observed_at":"2026-08-05T23:27:16.917353Z","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":"2026-08-05T23:27:16.905732Z","title":"Goodfellow, and Rob Fergus","venue":null,"work_id":"055880b2-9fff-48bb-b7f0-64bb5c87ea6b","year":2014},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.727031Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:b900d808d9d4aa92b7a383b5570d4fd855a11dcd470d4b66efdb7c4f45b62864","observation_id":"5edc0aed-92cd-4c5c-ac87-83436b09311a","resolution":{"observed_at":"2026-08-05T23:27:16.908840Z","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":"2026-08-05T23:27:16.896181Z","title":"Fca: Learning a 3d full-coverage vehicle camouflage for multi-view physical adversarial attack","venue":null,"work_id":"09b157bd-116d-4681-951a-804d85e55297","year":2022},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.729717Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:383fc70c4c2c60f7c3607222525f1e48dd718a77329b5d37f54ed9a3c0f72beb","observation_id":"a02bcf73-a696-4330-93ea-16c5ee50faa0","resolution":{"observed_at":"2026-08-05T23:27:16.899994Z","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":"1811.12641","last_updated":"2019-05-13T07:22:16Z","snapshot_observed_at":"2026-07-06T07:18:09.568026Z","submitted_at":"2018-11-30T06:55:22Z","title":"Transferable Adversarial Attacks for Image and Video Object Detection","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.12641","snapshot_observed_at":"2026-08-05T23:27:16.733195Z","title":"Transferable adversarial attacks for image and video object detection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.733195Z"},"links":{"cited_paper":"/paper/1811.12641","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:e74701c7a5bec2bd2c39ae149514c7257b5a9090532608d3b708c6ded5ac10ba","observation_id":"fce8aa18-4106-4f5b-9a88-529d22a022dc","resolution":{"observed_at":"2026-08-05T23:27:16.733195Z","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":"2026-08-05T23:27:16.886962Z","title":"Physical adversarial attack meets computer vision: A decade survey","venue":null,"work_id":"f817ce1b-5b7f-4d15-a763-19c925e27d45","year":2024},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.736686Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:3dcc3ce3f7cc2abd15adeae3d08e1e989ee9e178af804851cc560c982d7441cf","observation_id":"da2ad3e7-b1c3-418d-98e5-9c3e6d286543","resolution":{"observed_at":"2026-08-05T23:27:16.890098Z","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":"2026-08-05T23:27:16.878168Z","title":"Real-time kd-tree construction on graph- ics hardware","venue":null,"work_id":"517de3b8-be54-4017-ac3a-072c7790d0e7","year":2008},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.739957Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:0725adb5a8dae02899963d15df03dc1ee5a66e3da388b7db9993af0a902e978d","observation_id":"c3b6ea26-c0ae-4a0b-b9a4-2037714428e3","resolution":{"observed_at":"2026-08-05T23:27:16.881196Z","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":"2026-08-05T23:27:16.868880Z","title":"Deformable DETR: De- formable Transformers for End-to-End Object Detection","venue":null,"work_id":"02510fb3-2b8b-403f-bb54-d24594596ec6","year":2021},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.745847Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:50f211c4aa1ab712a069b5c40991808db24893144da254aeb4bdf5d45f7f94b4","observation_id":"933663af-bd89-46a6-a5b7-33f6f860859a","resolution":{"observed_at":"2026-08-05T23:27:16.872122Z","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":"2026-08-05T23:27:16.858301Z","title":"{TPatch}: A triggered physical adversarial patch","venue":null,"work_id":"577bed3d-fe5b-47c3-af0f-97d737b3a4d0","year":2023},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.748533Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:3eac401123cf2029930794ba8a8767b5d9b3d091e313ff8ddee1c36d775caa92","observation_id":"d20ad3c4-3f69-45a2-b00d-fbe481c9381a","resolution":{"observed_at":"2026-08-05T23:27:16.862428Z","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":"2026-08-05T23:27:16.849287Z","title":"Object detection in 20 years: A survey","venue":null,"work_id":"81b68a3c-fe07-415f-b75a-6676c394ce75","year":2023},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.751408Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:c7f15208cf66a18ab5478b8890a41018508560f33af3e9922a744fcc35f0312c","observation_id":"3666dbf3-f5bf-4637-bc90-7e8991284ee9","resolution":{"observed_at":"2026-08-05T23:27:16.852425Z","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":"2402.15853","last_updated":"2024-10-15T09:36:19Z","snapshot_observed_at":"2026-08-13T04:10:53.093416Z","submitted_at":"2024-02-24T16:50:10Z","title":"RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation","version":2},"cited_work":{"arxiv_id":"2402.15853","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.15853","snapshot_observed_at":"2026-08-05T23:27:16.773484Z","title":"RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation","venue":"cs.CV","work_id":"d6aae56e-05df-44d4-b497-0c705f5e69b4","year":2024},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.742710Z"},"links":{"cited_paper":"/paper/2402.15853","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:43f38cdb8c7482aac770caace7d20bd5e99e39dc1d32e736455e8497a05f03ce","observation_id":"cfa4c5b3-3264-4927-8b47-4d363e7ac66a","resolution":{"observed_at":"2026-08-05T23:27:16.779123Z","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":"2026-08-05T23:27:17.000151Z","title":"Efficient adversarial attacks for visual object tracking","venue":null,"work_id":"6f6ddfc6-95ec-4110-b19d-e68266390253","year":2020},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.683798Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:230ea1a648971c0a851fbe5615047093422f1f9111dc617cfb65602da171ca8e","observation_id":"66604a1f-f7f4-4709-97ad-ded26d22ea39","resolution":{"observed_at":"2026-08-05T23:27:17.003099Z","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":"2026-08-05T23:27:17.024947Z","title":"Mask r-cnn","venue":null,"work_id":"34b3e0b1-6611-4529-bc7c-2f9f101eb04b","year":2017},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.671985Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:0d423e5647a58ed179e5c25290b6a9232b7bed6fd957c2a880bc33ce36251b50","observation_id":"153a8094-f503-4a96-b43f-31abdcb4bb64","resolution":{"observed_at":"2026-08-05T23:27:17.027757Z","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":"2026-08-05T23:27:17.041662Z","title":"YOLOX: Exceeding YOLO Series in","venue":null,"work_id":"5844f07b-d030-460b-8e12-ad40c839c642","year":2021},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.659511Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:f288dff3bc6cb67d2e6fa8be7a7fe2a971bef52f66c23f8fcd75425abb63c6c4","observation_id":"2afa9f11-14b8-4d36-9a68-9e8a2b0e5055","resolution":{"observed_at":"2026-08-05T23:27:17.044885Z","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":"2026-08-05T23:27:16.955916Z","title":"{X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection","venue":null,"work_id":"1c943ddd-bfc4-478b-949a-eb35e51f8608","year":2023},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.705228Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:82594a8d9c9ed8701934abd5404664f6bacb12fbfc8da287b7a6d1a9308552f6","observation_id":"d71e321b-3fce-451c-81c3-d84502dda6af","resolution":{"observed_at":"2026-08-05T23:27:16.958708Z","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":"2026-08-05T23:27:17.059902Z","title":"Centernet: Keypoint triplets for object detection","venue":null,"work_id":"358055fb-b932-4748-93af-1f4123bbe7e1","year":2019},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.653170Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:2c48d323b1d222dd6bf2ce126c2da23f675b39c1bcf3e5d5e788c580044c084d","observation_id":"73ac68d9-fa4d-4d9a-a5b7-53a9e7d34de3","resolution":{"observed_at":"2026-08-05T23:27:17.063397Z","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":"2405.07595","last_updated":"2024-05-13T09:56:57Z","snapshot_observed_at":"2026-08-13T00:09:04.836655Z","submitted_at":"2024-05-13T09:56:57Z","title":"Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07595","snapshot_observed_at":"2026-08-05T23:27:16.677578Z","title":"Environmental matching attack against un- manned aerial vehicles object detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.677578Z"},"links":{"cited_paper":"/paper/2405.07595","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:9f0ef49a3d4520e56cb1e1e7f00e38f328d1d7191f08dc2ef998fe7fb622c002","observation_id":"f99e0a75-03c1-4ceb-9a1e-55bf6b96a24c","resolution":{"observed_at":"2026-08-05T23:27:16.677578Z","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":"2026-08-05T23:27:17.078465Z","title":"Moderngl, high perfor- mance python bindings for opengl 3.3+","venue":null,"work_id":"212c7592-bbc7-4df7-b75c-aa8da32579d8","year":2020},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.646958Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:2f13dec4e24c872df7a65093e8bba94bd593f5fba0a88d6694e7e3a6ed3ce3ec","observation_id":"24f8a5a1-97c3-4e37-83b3-5c637f29eb7f","resolution":{"observed_at":"2026-08-05T23:27:17.082299Z","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":"1906.07155","last_updated":"2019-06-17T17:58:12Z","snapshot_observed_at":"2026-08-03T13:01:27.142233Z","submitted_at":"2019-06-17T17:58:12Z","title":"MMDetection: Open MMLab Detection Toolbox and Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.07155","snapshot_observed_at":"2026-08-05T23:27:16.643508Z","title":"MMDetection: Open MMLab Detection Toolbox and Benchmark","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.643508Z"},"links":{"cited_paper":"/paper/1906.07155","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:d8a3f8252f7ba8d2a5a93fa28016098b4462f979394d4fe0d76e87831320a265","observation_id":"cacd8c97-7858-4c73-966b-47085c4d5579","resolution":{"observed_at":"2026-08-05T23:27:16.643508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-08-05T23:27:16.662274Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.662274Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:f0ad53a00f410428d610fbf3a688986ce734f2eb6527e518e93c9dcece2039a2","observation_id":"77b52fc8-dca5-4cbe-be09-a368cdc53ff5","resolution":{"observed_at":"2026-08-05T23:27:16.662274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08970","last_updated":"2022-01-22T06:00:17Z","snapshot_observed_at":"2026-08-05T14:04:42.818320Z","submitted_at":"2022-01-22T06:00:17Z","title":"Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08970","snapshot_observed_at":"2026-08-05T23:27:16.689453Z","title":"Parallel rectangle flip attack: A query-based black-box attack against object detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.689453Z"},"links":{"cited_paper":"/paper/2201.08970","citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:fcf1d1ae39d2049a17752307419dde6aa1cf0eecf5a9d7efee0df42b79a09399","observation_id":"ac66848a-75df-49cf-ad9e-6c76115f3964","resolution":{"observed_at":"2026-08-05T23:27:16.689453Z","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":"2026-08-05T23:27:16.947701Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":"4a56d916-4dd5-42af-9cb6-4d04df237694","year":2017},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.708030Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:0995a6fa948d6f09c4d288204e9fef76c5b929c93d20a67483adc1b1da93b67e","observation_id":"e9508c1f-affe-48ca-a634-f4648ec5b684","resolution":{"observed_at":"2026-08-05T23:27:16.950470Z","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":"2026-08-05T23:27:17.008417Z","title":"Gram-schmidt orthogonalization: 100 years and more","venue":null,"work_id":"3b4922fb-5a2e-44eb-a7e0-85273ac043cb","year":2013},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.681043Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:f08e42201a7bfd8f294071de229ea30a7f8d59fef9eda354fe7387798db186a5","observation_id":"0534d708-280e-44e0-b749-815fa2f1aa8d","resolution":{"observed_at":"2026-08-05T23:27:17.011448Z","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":"2026-08-05T23:27:16.963622Z","title":"Ssd: Single shot multibox detector","venue":null,"work_id":"10e6a5b9-a31a-4463-bd12-fd985616bf95","year":2016},"citing_paper":{"arxiv_id":"2508.05414","last_updated":"2025-08-07T14:07:49Z","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T23:27:16.701522Z"},"links":{"citing_paper":"/paper/2508.05414"},"observation_digest":"sha256:38db36e80b6acccb6c120eb7766a7b515a0e63c83173a978ade042b4cebbfa58","observation_id":"a6066994-22ac-4fb0-b9ff-94e41995e6b4","resolution":{"observed_at":"2026-08-05T23:27:16.967351Z","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":"2508.05414","last_updated":"2025-08-07T14:07:49Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T10:24:18.475242Z","submitted_at":"2025-08-07T14:07:49Z","title":"Physical Adversarial Camouflage through Gradient Calibration and Regularization"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":37},"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 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2508.05414."}