{"as_of":"2026-08-07T22:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b01a1a7ad1adf3c3cf693c1fbc33bb17a9eb6581948bdb0dd3ac736a4678d842","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:37.174665Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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":"2026-05-10T15:58:28.202606Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:31:05.046964Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"cited_work":{"arxiv_id":"2505.24227","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.24227","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"12f6a2aa-be18-45cb-ba3b-bad8df2a5ba5","year":2025},"citing_paper":{"arxiv_id":"2604.12833","last_updated":"2026-04-14T14:52:15Z","snapshot_observed_at":"2026-07-06T23:00:56.449281Z","submitted_at":"2026-04-14T14:52:15Z","title":"Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T15:58:28.202606Z"},"links":{"cited_paper":"/paper/2505.24227","citing_paper":"/paper/2604.12833"},"observation_digest":"sha256:cc786195913b49a3ee401d70c99313300d735206c4f6378837e7966e0e5ea545","observation_id":"ab4d6459-03a9-46c2-b864-be24593e8fb0","resolution":{"observed_at":"2026-05-11T09:31:05.052907Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.24227/citation-record","integrity":"/paper/2505.24227/integrity","json":"/paper/2505.24227/citation-record.json","paper":"/paper/2505.24227"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.03050","last_updated":"2021-07-07T07:22:41Z","snapshot_observed_at":"2026-07-06T11:26:50.480422Z","submitted_at":"2021-07-07T07:22:41Z","title":"Controlled Caption Generation for Images Through Adversarial Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03050","snapshot_observed_at":"2026-08-07T12:35:34.858781Z","title":"Con- trolled caption generation for images through adversarial attacks.arXiv preprint arXiv:2107.03050,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:34.858781Z"},"links":{"cited_paper":"/paper/2107.03050","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:64f2155ad7caaef988eba83c6180e11edf8060168b17bfa91acda23fb9d8a240","observation_id":"96877b59-a946-49e9-81a3-54fa8c3eed8d","resolution":{"observed_at":"2026-08-07T12:35:34.858781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11940","last_updated":"2024-12-11T22:32:49Z","snapshot_observed_at":"2026-08-07T21:59:47.751674Z","submitted_at":"2024-02-19T08:27:23Z","title":"AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization","version":4},"cited_work":{"arxiv_id":"2402.11940","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.11940","snapshot_observed_at":"2026-08-07T12:35:37.298481Z","title":"AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization","venue":"cs.CV","work_id":"1f1e3349-eddd-4ee7-abf8-58e7a54e6cbe","year":2024},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.991885Z"},"links":{"cited_paper":"/paper/2402.11940","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:b1026d8a9d3fbe278f0f155569fa97a1f71483dc03258f3c130245b983928f9a","observation_id":"471cbf59-d131-4b80-8c97-d3783b4df21e","resolution":{"observed_at":"2026-08-07T12:35:37.349893Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07490","last_updated":"2019-12-03T19:30:19Z","snapshot_observed_at":"2026-08-06T03:44:36.669916Z","submitted_at":"2019-08-20T17:05:18Z","title":"LXMERT: Learning Cross-Modality Encoder Representations from Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07490","snapshot_observed_at":"2026-08-07T12:35:36.557475Z","title":"and Bansal, M","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.557475Z"},"links":{"cited_paper":"/paper/1908.07490","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:41e26b6bff10787731e72528b24077892ffecbe2236427b522f5eb7998e8c499","observation_id":"438c66c2-4e98-4e3b-b30a-6a4730ea0a76","resolution":{"observed_at":"2026-08-07T12:35:36.557475Z","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":"2026-08-07T12:35:38.479930Z","title":null,"venue":null,"work_id":"144c9688-d335-4321-a3eb-cfd33647986d","year":2025},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.636443Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:4e0505cff86d27e79a3db46f33c1769b1dc35b880d67db5f0b5c340eba32b36d","observation_id":"70145193-9325-469e-be29-ff284fd76d58","resolution":{"observed_at":"2026-08-07T12:35:38.631733Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:38.333728Z","title":"During the optimization procedure, let Ii denote the generated adversarial image at the ith step","venue":null,"work_id":"4f2937d7-c67b-4be7-890a-6125fb2a4001","year":2014},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.737324Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:6f1478c3b3c67f9686958674c7f704cc37712057cb8a0f27332906497001bf31","observation_id":"f85a36c9-787c-4b5f-8d7e-01acd4b2bcee","resolution":{"observed_at":"2026-08-07T12:35:38.399856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:37.935808Z","title":null,"venue":null,"work_id":"678a3c22-f89f-4686-86ec-4098415c7ff0","year":2018},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.957128Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:636806cb7af853cc33038e1351111265d91630dfdad5d9d1ea324f320809cba8","observation_id":"c4cac3ac-6a5b-485c-bfd3-b6b516853df9","resolution":{"observed_at":"2026-08-07T12:35:38.025899Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:37.731140Z","title":null,"venue":null,"work_id":"eddc1de5-89cb-4d3f-8ea6-555a74abc80f","year":2021},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.055647Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:b1dd2a2c080562e8034edba84ba64c1797bad05fc191d0d5a635a2fe3d6090e0","observation_id":"d132cbeb-ebc9-4e8a-a7a6-70314acf16b1","resolution":{"observed_at":"2026-08-07T12:35:37.815825Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:37.587259Z","title":"APA measures the percentage of successful prediction answers among all the images","venue":null,"work_id":"47b3ca72-4eee-4d0d-8126-220b12ee44d0","year":2012},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.174665Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:6e431d79327c6e790aed86dfc965a4bf092c7379982884b77937774ac02f1090","observation_id":"8413f760-3128-46a4-bb36-7b2cd7a9db1c","resolution":{"observed_at":"2026-08-07T12:35:37.631326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.15221","last_updated":"2022-10-27T07:16:30Z","snapshot_observed_at":"2026-08-06T18:29:44.124523Z","submitted_at":"2022-10-27T07:16:30Z","title":"TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.15221","snapshot_observed_at":"2026-08-07T12:35:34.960220Z","title":"TASA: Deceiving question answering mod- els by twin answer sentences attack.arXiv preprint arXiv:2210.15221,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:34.960220Z"},"links":{"cited_paper":"/paper/2210.15221","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:974d4d4d0a130a7c0ee9d4b42a8a1b38c78e34ab1e21f35acd7a8a5acb0b7f52","observation_id":"5f2c3ffc-f350-41ea-9935-df696ddb5742","resolution":{"observed_at":"2026-08-07T12:35:34.960220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.09734","last_updated":"2021-11-18T14:49:15Z","snapshot_observed_at":"2026-07-06T12:09:52.161425Z","submitted_at":"2021-11-18T14:49:15Z","title":"ClipCap: CLIP Prefix for Image Captioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.09734","snapshot_observed_at":"2026-08-07T12:35:36.351321Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.351321Z"},"links":{"cited_paper":"/paper/2111.09734","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:34759265c24a6ec1272d6513b9e70cda710b45dc075a5b537346f0a318fd3733","observation_id":"22bc1e94-042a-402c-aa38-1f117cddb53e","resolution":{"observed_at":"2026-08-07T12:35:36.351321Z","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":"2026-08-07T12:35:38.146693Z","title":"In this study, we randomly choose 1,000 images from the test set of the above datasets as clean images to craft adversarial examples","venue":null,"work_id":"30440b38-f7fd-44d7-813a-e576413690e4","year":2023},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.862764Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:e345c52bca986f62d096f1193fad395fff390be5d35ab6009245e5d9fb865fe4","observation_id":"ffba26f6-b460-4727-8216-1ab2f21a672f","resolution":{"observed_at":"2026-08-07T12:35:38.234101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20090","last_updated":"2025-07-21T08:41:47Z","snapshot_observed_at":"2026-08-07T03:14:52.968293Z","submitted_at":"2024-05-30T14:27:20Z","title":"Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20090","snapshot_observed_at":"2026-08-07T12:35:35.084001Z","title":"Typography leads semantic diversifying: Amplify- ing adversarial transferability across multimodal large lan- guage models.arXiv preprint arXiv:2405.20090,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.084001Z"},"links":{"cited_paper":"/paper/2405.20090","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:1eaa368465deaaa20d774e8e70708fac17a6475c120c1cc1b376ca3193c3873e","observation_id":"f60c9835-8292-467e-adf1-c4af10ca95ea","resolution":{"observed_at":"2026-08-07T12:35:35.084001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17626","last_updated":"2024-05-02T00:08:36Z","snapshot_observed_at":"2026-07-06T16:39:08.850847Z","submitted_at":"2023-10-26T17:45:26Z","title":"A Survey on Transferability of Adversarial Examples across Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17626","snapshot_observed_at":"2026-08-07T12:35:35.411237Z","title":"A survey on transferabil- ity of adversarial examples across deep neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.411237Z"},"links":{"cited_paper":"/paper/2310.17626","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:f311c7cea892385f71285e6316f07adaea9488da7fb95de0b957f6a3a8d73601","observation_id":"bd228681-debf-4fbd-884c-44a6458b6f6d","resolution":{"observed_at":"2026-08-07T12:35:35.411237Z","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":"2026-08-07T12:35:38.725136Z","title":"S., Oh, C., and Cavallaro, A","venue":null,"work_id":"da03faf1-4ecf-4aeb-8302-062d0f6cab6a","year":1902},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.415446Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:95cf62ccef3a162313c577f029a06f9e02daa15efa06245f0d14996d62ec9edb","observation_id":"5699b861-47e4-4f3f-97d4-bcb9eaa6e1f2","resolution":{"observed_at":"2026-08-07T12:35:38.788221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04403","last_updated":"2023-12-07T16:16:50Z","snapshot_observed_at":"2026-08-02T17:13:23.195883Z","submitted_at":"2023-12-07T16:16:50Z","title":"OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04403","snapshot_observed_at":"2026-08-07T12:35:35.568500Z","title":"OT-Attack: Enhancing adversarial transferability of vision-language models via optimal transport optimiza- tion.arXiv preprint arXiv:2312.04403,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.568500Z"},"links":{"cited_paper":"/paper/2312.04403","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:abb7d5fb75d23aafb55586e3728913f8fde786aaae6d1b188b635a9ad6f31cc8","observation_id":"87da1941-05b3-4541-bca0-b79fd8f91043","resolution":{"observed_at":"2026-08-07T12:35:35.568500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04913","last_updated":"2023-12-08T09:08:50Z","snapshot_observed_at":"2026-08-07T21:16:58.470052Z","submitted_at":"2023-12-08T09:08:50Z","title":"SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04913","snapshot_observed_at":"2026-08-07T12:35:35.832321Z","title":"SA- Attack: Improving adversarial transferability of vision- language pre-training models via self-augmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.832321Z"},"links":{"cited_paper":"/paper/2312.04913","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:133255401c6df1a3c36cbfa29f83fa76ba98a0c266b06450d31fe58d7c7f1c1d","observation_id":"94ac1984-3a7a-4856-a826-f280259693f9","resolution":{"observed_at":"2026-08-07T12:35:35.832321Z","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":"2026-08-07T12:35:38.907718Z","title":"Adversarial VQA: A new benchmark for evaluating the robustness of VQA models","venue":null,"work_id":"9e344104-fb94-4dfc-a5ba-5cd78966a0ab","year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.205207Z"},"links":{"citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:8764f2a0c89adacad655478bfebfe0ce68cfe65608f4cdd65b9de2984e0d3ceb","observation_id":"b3c11b9b-a659-4a2e-9bf5-d6e7fd6315e6","resolution":{"observed_at":"2026-08-07T12:35:38.976559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.06732","last_updated":"2018-07-20T01:57:37Z","snapshot_observed_at":"2026-08-03T00:10:21.477247Z","submitted_at":"2018-07-18T01:17:27Z","title":"Motivating the Rules of the Game for Adversarial Example Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.06732","snapshot_observed_at":"2026-08-07T12:35:35.259685Z","title":"P., Goodfellow, I., Andersen, D., and Dahl, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:35.259685Z"},"links":{"cited_paper":"/paper/1807.06732","citing_paper":"/paper/2505.24227"},"observation_digest":"sha256:98ab435f4eb8f06a11a8012bcc42c05a53b7693c456ce65840895d775702968f","observation_id":"6218c211-9f0b-4491-a79a-0356d7360607","resolution":{"observed_at":"2026-08-07T12:35:35.259685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.24227","last_updated":"2025-05-30T05:30:02Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T21:17:50.268245Z","submitted_at":"2025-05-30T05:30:02Z","title":"Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":18},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2505.24227."}