{"as_of":"2026-08-12T15:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c8f5f97115f9e63d5f5d63c5785a5887a5af613297ac2900b50e8b1e3eb9906b","coverage":[{"denominator":71,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":71,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:15:51.715759Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2507.03427/citation-record","integrity":"/paper/2507.03427/integrity","json":"/paper/2507.03427/citation-record.json","paper":"/paper/2507.03427"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:15:53.088005Z","title":"Towards improving robustness of deep neural networks to adversarial perturbations","venue":null,"work_id":"c6d2770a-e38b-4454-85be-5d654dfab3d6","year":1903},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.377154Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:8ca1be24d3a63d91c47eda57f22d77962aa3e3b1a53ceb84a339e51f47925bbb","observation_id":"0e1ec4f2-517a-4afe-9316-8f6c648d8334","resolution":{"observed_at":"2026-08-06T20:15:53.093143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:53.071186Z","title":"Square attack: a query-efficient black-box adversarial attack via random search","venue":null,"work_id":"5d793da2-2c21-43e4-8a13-0563557acefe","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.383393Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:3405bd6187608bd1cb4c4a6b3e66e89c7cfdcabd5138d6b8e98d395dd2e5e278","observation_id":"a2de184e-49b6-4cf4-ae99-baf8aa154800","resolution":{"observed_at":"2026-08-06T20:15:53.076613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:53.053335Z","title":"Defense against adversarial attacks using dragan","venue":null,"work_id":"38008604-e53e-4042-9dc4-ef19c08839dd","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.388228Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:01c5a66cbcab7846fe2c2934b25af006fb1d7c3a7aafcc7dd20452f14f3078be","observation_id":"f744d7cf-cca5-423c-b547-8f0b4763fbd9","resolution":{"observed_at":"2026-08-06T20:15:53.059205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:53.036355Z","title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","venue":null,"work_id":"0307121b-85c6-451e-aaa5-9617f1571317","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.393390Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:71da1e80a34e7bf07530dc775bdfa50829c02a6bda01161a3ff970bab4119193","observation_id":"d8572fcc-ecee-4b2f-be09-104825b2749c","resolution":{"observed_at":"2026-08-06T20:15:53.041670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.398504Z","title":"Synthesizing robust adversarial examples","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.398504Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:ef4e24fbd70e612afae8fc8a373017020bdc13170c078eb33dd51a5ca759a1a2","observation_id":"b092950a-575d-49ad-89e8-b5bf86ab83f0","resolution":{"observed_at":"2026-08-06T20:15:51.398504Z","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":"2026-08-06T20:15:53.006551Z","title":"Parameter-free online test-time adaptation","venue":null,"work_id":"67322b0d-466a-45e1-9ee1-7b63102225d3","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.403724Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:29111465bb87b85d50c55d237d1373f09fd9e17b7a922033c0d7e44a38f83a2d","observation_id":"0c402aaf-fdd9-4614-8eec-08a2c020fe94","resolution":{"observed_at":"2026-08-06T20:15:53.012047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.409209Z","title":"Towards evaluating the robustness of neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.409209Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:000c06d6ba0a8394e05b94745742eaedeb836cfbc782c56c5bd91afe7ff8749d","observation_id":"f18aecca-9f84-4a4e-bcd0-e8c4878a0821","resolution":{"observed_at":"2026-08-06T20:15:51.409209Z","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":"2026-08-06T20:15:52.976734Z","title":"Robust classification via a single diffusion model","venue":null,"work_id":"e9717e29-dfd4-4682-8bfc-d5308ef176a5","year":2024},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.414082Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:52d8a333f81d2214ef28c4d61790c51945e92991a9f623e6375ecd7a7f4a2ce0","observation_id":"ea47205f-344f-44b6-a5ec-306bde9fb56e","resolution":{"observed_at":"2026-08-06T20:15:52.982707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.958293Z","title":"Robust overfitting may be mitigated by properly learned smoothening","venue":null,"work_id":"7598f632-7a9f-4638-82b7-fc745f2e526b","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.418863Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:4426ba6c7c3f6a8ea7e93a775b4a00f56a3cbc8752113a4920f22199bc02fcc3","observation_id":"9e7350ed-b093-46fe-9e41-eb85486a7936","resolution":{"observed_at":"2026-08-06T20:15:52.964169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.939512Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"dc2144f2-5e99-44e4-ba59-9c3428d5f7ea","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.424574Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:541460e56629f7e42514a83d2a151bdd5234a66f89d76f88b675bddeb782408a","observation_id":"d10de621-cb3a-4dc2-9946-4b04b6c03747","resolution":{"observed_at":"2026-08-06T20:15:52.945955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.920648Z","title":"Evaluating the adversarial robustness of adaptive test-time defenses","venue":null,"work_id":"a2593b95-f90d-4747-bb69-cffc591b9626","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.429542Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a9f8d15d383039793aa95b4f27f6e3980026b1d5662fe861010afce56a222b73","observation_id":"fb3acd87-cd4d-480f-a2a2-dd21ffee5a2c","resolution":{"observed_at":"2026-08-06T20:15:52.926621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.901038Z","title":"Minimally distorted adversarial ex- amples with a fast adaptive boundary attack","venue":null,"work_id":"a890b0df-f1b1-4517-8682-539201db9cc3","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.434611Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:7d257f8d757717359c8458befb499f66f244125ee403b831f89087879437e21a","observation_id":"b037764e-57be-4336-8049-1c4857bd615e","resolution":{"observed_at":"2026-08-06T20:15:52.907482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.881590Z","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","venue":null,"work_id":"77bb0ace-a594-4710-8850-825a098aeeb3","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.439438Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:7b7326194173d81b048f224bf5a1df706c13fecb3772a5618b0fa09a7336c261","observation_id":"dd26f23b-e6b8-48ac-a6f2-f3f840e38f83","resolution":{"observed_at":"2026-08-06T20:15:52.887431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.861370Z","title":"Libre: A practical bayesian approach to adversarial detection","venue":null,"work_id":"26feab1e-eecf-4e80-9338-0f3f3f0732f6","year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.445839Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:21604db9e182a77443df837174ad05c67f1e00b112442ab23727c3c70ad5162f","observation_id":"ce12d911-2de1-461c-9fea-a30d4c8fcfbd","resolution":{"observed_at":"2026-08-06T20:15:52.867160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.842376Z","title":"The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training","venue":null,"work_id":"0606af0f-e312-4e16-85e7-88a6bb552252","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.451697Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:487883c819faa424bdf3eaff82de6006c75b18dc376db4ce732aab0b14b3a3c0","observation_id":"1e9d7dde-04b6-45ef-acf7-6a4fc119e08f","resolution":{"observed_at":"2026-08-06T20:15:52.848756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.823268Z","title":"Boosting adversarial attacks with momentum","venue":null,"work_id":"f623613c-3e49-40e4-a30d-a7ce55c50dc4","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.457141Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a6b6d572f4e2ce754b5172f6f21b7f8171781015affd505565c86da0835e87bc","observation_id":"77896a0d-6ee0-413d-8e61-c00fa3bbce63","resolution":{"observed_at":"2026-08-06T20:15:52.829109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.802985Z","title":"Enhancing the robustness of neural collaborative filtering systems under malicious attacks","venue":null,"work_id":"ca629346-ee89-4c62-92b8-12e813adb865","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.462599Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:0c7a9a3136c650dce83eafbb17d4845c1ba76174633c56d96cabb291ec333c8a","observation_id":"93ba0521-7ec2-4237-a510-b493a5b82940","resolution":{"observed_at":"2026-08-06T20:15:52.809910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.784261Z","title":"Unsupervised image captioning","venue":null,"work_id":"51ef66b2-069a-4825-a9df-a361c1755aa2","year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.467979Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:fff88ecfa42091b223eac20ea599ee6e287f611dd0a50d625d7f6d55e8f499ab","observation_id":"c2d574a2-d3be-42da-965e-3db4fa0e3e0e","resolution":{"observed_at":"2026-08-06T20:15:52.790155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.763737Z","title":"Push & pull: Transferable adversarial examples with attentive attack","venue":null,"work_id":"17e02aab-c6bd-43f9-b031-ddd99245fb05","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.472778Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:1be2269f1982c9adfd5ceb873bdc3da543ccf04c9bf8295beb92774d3b3027ef","observation_id":"f6819be6-aaba-44f8-9c6d-a7bb569f5955","resolution":{"observed_at":"2026-08-06T20:15:52.771019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.741740Z","title":"Unsupervised representation learning by predicting image rotations","venue":null,"work_id":"cfb2ac52-ff63-4100-86a0-33900b71f56a","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.477915Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:023eeb00e9357310951a49c4fbd4bff1c1de0928a32e0e0c6de88808c5549116","observation_id":"520c084b-37d0-46f9-a653-227d4f60f7af","resolution":{"observed_at":"2026-08-06T20:15:52.748035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.482438Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.482438Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a444ec030e60bcda9ef34c87ed41ac158ef03792e10c359ab145b4367ec5d0b0","observation_id":"1784f7b5-4392-4343-bb2b-369353a18e25","resolution":{"observed_at":"2026-08-06T20:15:51.482438Z","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":"2026-08-06T20:15:51.486862Z","title":"Momentum contrast for unsupervised visual representation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.486862Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:cb79cc694aa588acb8702cb79f0110b32eda0369c2a38747df2e034f08fea80b","observation_id":"98ed925a-67d5-4a46-bc17-e6ddd18cbbd8","resolution":{"observed_at":"2026-08-06T20:15:51.486862Z","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":"2026-08-06T20:15:51.491394Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.491394Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:73434c7bb9af89aaffdd8d2d9f0674aedd9f2609612f667a8e9e38796a1587c6","observation_id":"671d2d3a-36cf-4ac0-a427-fb71aa520d2b","resolution":{"observed_at":"2026-08-06T20:15:51.491394Z","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":"2026-08-06T20:15:52.682190Z","title":"Identity mappings in deep residual networks","venue":null,"work_id":"f3145459-fd12-47d5-aec3-ef749ea30386","year":2016},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.496025Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:78bac39196e835372baec6715b1bc90c65918cca73a2518c6a07c81f1d7d542e","observation_id":"5d60fcae-01cd-42d4-87d0-626c9eb0dd54","resolution":{"observed_at":"2026-08-06T20:15:52.690163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.661798Z","title":"Aid-purifier: A light auxiliary network for boosting adversarial defense","venue":null,"work_id":"94b98bc9-41b2-4d5f-9eef-a99622e6f850","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.500564Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:981e144d559ce679f7acdc75f9a1654ac58e4bcccb1b6407a118b352e01e039a","observation_id":"89f45235-1221-4859-ad91-b8e4187e8bdb","resolution":{"observed_at":"2026-08-06T20:15:52.668651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.639751Z","title":"Puvae: A variational autoencoder to purify adversarial examples","venue":null,"work_id":"adc9e65e-f5b4-4117-9962-a72a93ec1ae4","year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.505309Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:9080af5f9518779ff9415d08a164c5ef7d670693913d58ac4b9cc195ffb00c58","observation_id":"f11a85d7-be41-442d-b179-93a3f9f56bcf","resolution":{"observed_at":"2026-08-06T20:15:52.646685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.509590Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.509590Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:e16ea67c4236fd2c9dce0f61b2c98320f68195d0c26224a92afa00292eedadba","observation_id":"e1b1678a-3022-44e8-982b-dde12bdedd2e","resolution":{"observed_at":"2026-08-06T20:15:51.509590Z","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":"2026-08-06T20:15:52.604248Z","title":"Adversarial machine learning at scale","venue":null,"work_id":"cacc91b2-ecb1-41de-a5b9-cb6f4c084627","year":2017},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.514332Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:295f06c216a6fd72e386beef91bd500ff3de4a85e9862fa41e8f56f28ead0d96","observation_id":"67c34174-e402-43ab-9173-a2272410601a","resolution":{"observed_at":"2026-08-06T20:15:52.610542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.580100Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":"1f7fa5e6-40c3-48a7-9b52-bed620e0bf8c","year":1998},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.518677Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:6af36492fa1f99e0fafd9320668ef74f44b6741145537c9f0fac8497e7de0605","observation_id":"790e197d-7c3d-4ecd-8b53-f171222a5c20","resolution":{"observed_at":"2026-08-06T20:15:52.589034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.558211Z","title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","venue":null,"work_id":"1420519a-0fc8-434a-8ecc-b46ebb14c96b","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.523239Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:5fa0087648dd4c35eae9c0cf9ff9bf3c56df7a38a7f446ee5f09e5de7582b03f","observation_id":"2237fe5f-7b76-4100-ac3c-67b3fcf53a09","resolution":{"observed_at":"2026-08-06T20:15:52.565328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.540451Z","title":"Robust evaluation of diffusion-based adversarial purification","venue":null,"work_id":"f58f2433-453d-4b96-aa43-861706e32eca","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.527953Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:7bf58ee3d5c2e4744c5a63de8b460e2bae86a631a85be9dc9aec45dd72342859","observation_id":"5bf1d585-4249-411c-9fd8-1e1afd9339ce","resolution":{"observed_at":"2026-08-06T20:15:52.545997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.523146Z","title":"Learn- ing defense transformations for counterattacking adversarial examples","venue":null,"work_id":"c69308c5-1c16-4941-bf03-9efc3874d924","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.532530Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:6910b621b75edc27364661b136c5225ac4138132b0b1f5fbc6f423fc68591236","observation_id":"de2e7519-d7d9-46c4-a090-c8af9ff023a2","resolution":{"observed_at":"2026-08-06T20:15:52.528801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.504128Z","title":"Dual manifold adversarial robustness: Defense against lp and non-lp adversarial attacks","venue":null,"work_id":"807187a8-f9bc-4b39-b35c-1b41300bc88e","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.537587Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:6591bc93e0190b21967022544c0d342ae805a13669b679746e0aa919504c52a0","observation_id":"3637b4a9-cb9e-452b-ae8f-ddf8c2a61d28","resolution":{"observed_at":"2026-08-06T20:15:52.510469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.484529Z","title":"Characterizing adversarial subspaces using local intrinsic dimensionality","venue":null,"work_id":"eb6015b0-2c13-4828-af3e-ed4402fd1ba5","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.542912Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:323dbbe0be91c3eea6f1aa85e704adab3402948748416983e813dbac19d25c87","observation_id":"620b6be5-7f72-42c2-870f-d21448a0023c","resolution":{"observed_at":"2026-08-06T20:15:52.490012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.547373Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.547373Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:c4e58f3a9ac0e29101566b92391ce561b99d0096da89df8f84f2e43e3d48034d","observation_id":"22359473-feb6-4c09-8562-fc6a036b9208","resolution":{"observed_at":"2026-08-06T20:15:51.547373Z","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":"2026-08-06T20:15:52.451312Z","title":"Adversarial attacks are reversible with natural supervision","venue":null,"work_id":"96723aa2-f405-4c66-b97d-f84ef765e01b","year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.552414Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:8f53994323deed4350b0a34302ea6a5e6f0996dd6decc1b07296474557a9ce17","observation_id":"28e43b70-d2d4-4248-9ec9-f4596f4907b2","resolution":{"observed_at":"2026-08-06T20:15:52.457470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.433953Z","title":"Guessing and entropy","venue":null,"work_id":"ceba9b81-86f7-4f16-aa4d-fa3d5a73c750","year":1994},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.556847Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:4ba83537c3d86a2636fb2a6183dc201c61981160fa3aa8e7214af8c36aeaafe5","observation_id":"18246bba-2abd-488f-ba10-57d7a6efc710","resolution":{"observed_at":"2026-08-06T20:15:52.439409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.417206Z","title":"Toward robust sensing for autonomous vehicles: An adversarial perspective","venue":null,"work_id":"9ed8c1e1-6843-4b50-a5e1-8f99d03a36f3","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.561304Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:8a648266596b595289cb16cf644b61aef2c41793db6f57222465bcae0e2dd2e5","observation_id":"d4f37da9-614b-4e92-bfd9-bc0f984caec0","resolution":{"observed_at":"2026-08-06T20:15:52.422374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.399873Z","title":"Deepfool: a simple and accurate method to fool deep neural networks","venue":null,"work_id":"dc7f74ad-efbb-4447-9163-f9e1b35a20bf","year":2016},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.565713Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:9a45b3b6cdc3bdf6ac6b31eb267ae895be0bb28f1de18d1972e639c772a3dac9","observation_id":"5320ce52-4a75-4a26-af86-8929bbd08645","resolution":{"observed_at":"2026-08-06T20:15:52.405862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.379577Z","title":"Diffusion models for adversarial purification","venue":null,"work_id":"f8bc4fc9-2e51-4471-9620-498b06daa4bb","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.570596Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:46c0d7bd1a7c446d432ea4cb9f33e2d881f6c99c69a6985d7a8cd5514750a154","observation_id":"e6fdfe4d-522c-43f0-8e55-16a1fd67bc36","resolution":{"observed_at":"2026-08-06T20:15:52.386536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.357279Z","title":"Overfitting in adversarially robust deep learning","venue":null,"work_id":"0b8b15a2-d689-4c65-a862-ac46f259b076","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.575346Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:becb857db1d610621eed5df1293c1466d35d059e7a044e8efe2f559c4bc1de5d","observation_id":"bc685805-3ea7-4724-a6dd-6c85d604469c","resolution":{"observed_at":"2026-08-06T20:15:52.364497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.338541Z","title":"Defense-gan: Protecting classifiers against adversarial attacks using generative models","venue":null,"work_id":"29aa8826-8597-495c-a1b1-35f016cf8b04","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.580205Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:865d985b9cde75499cc900de3519c3be2efe9b8509b404c7b7081a3ee30e9075","observation_id":"2e9c7b1d-644e-410a-a453-87c9512ab747","resolution":{"observed_at":"2026-08-06T20:15:52.344635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.319605Z","title":"Online adversarial purification based on self-supervised learning","venue":null,"work_id":"4a95827e-d8bd-43d7-934f-32e10ff23b35","year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.584899Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:d351ec90bf855b326e95ec7fe31de195e0d3303c18bd8fa556a7531387c77924","observation_id":"e721874e-a692-44f3-8927-3d0762e36902","resolution":{"observed_at":"2026-08-06T20:15:52.325213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.299001Z","title":"Pixeldefend: Leveraging generative models to understand and defend against adversarial examples","venue":null,"work_id":"5b813d0c-316b-44b2-a687-31c44e2dcbe9","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.589689Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:34d74caeaa13203182c6d3a5f4efd8fd75ee7dbc814ca06a62007cfa05dd32f0","observation_id":"a8a8df02-10ca-4e8a-a515-5fb65b4a249f","resolution":{"observed_at":"2026-08-06T20:15:52.305262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.281262Z","title":"Test-time training for out-of-distribution generalization","venue":null,"work_id":"1c3adc0e-c0c3-479e-9867-d0cd16440e40","year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.594330Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:9c39aa9cecd5411c23dc197cac9f27426e85f406e8ee1f297b38dc200bcb4982","observation_id":"f1139f10-ae81-4185-9e41-d96dcae8c3cb","resolution":{"observed_at":"2026-08-06T20:15:52.286569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-07-06T03:31:33.797310Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-06T20:15:51.598965Z","title":"Intriguing properties of neural networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.598965Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:ea59a64fbbf9f66730eef262de699c070782700cf22978662dc9ba62f0447ba8","observation_id":"c98463d0-246d-46a8-9b8f-762bccf6e798","resolution":{"observed_at":"2026-08-06T20:15:51.598965Z","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":"2026-08-06T20:15:52.258491Z","title":"Robust overfitting does matter: Test-time adversarial purification with fgsm","venue":null,"work_id":"6968eac3-4511-4c09-b8fa-033fb25b9275","year":2024},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.603758Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:2d458a322d177c0558d9220bd62e9345efb66e1ec75161ec9e51460925bcf29d","observation_id":"5c140c95-608a-471d-9d19-45ee91e624b1","resolution":{"observed_at":"2026-08-06T20:15:52.265117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12848","last_updated":"2023-04-02T21:27:16Z","snapshot_observed_at":"2026-08-06T20:07:13.043431Z","submitted_at":"2023-03-22T18:14:02Z","title":"Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder","version":3},"cited_work":{"arxiv_id":"2303.12848","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.12848","snapshot_observed_at":"2026-08-06T20:15:51.826490Z","title":"Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder","venue":"cs.CV","work_id":"6d8b6e4e-d3ef-4226-a738-07e31bcdd943","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.608337Z"},"links":{"cited_paper":"/paper/2303.12848","citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:b905c59b647edccb0b84451ae7bbb5ff31d6781fe3c99390f0765386663fefc9","observation_id":"dc6cb721-f421-49d1-a4e2-ac62e7cf5515","resolution":{"observed_at":"2026-08-06T20:15:51.836116Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.239049Z","title":"Average gradient-based adversarial attack","venue":null,"work_id":"063061fa-b2ba-4177-b768-f6968fed3fb2","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.613257Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a46c05c12eaceebf11a13d3c98011c45b93f085503e5283b993863c74345381d","observation_id":"c47a3e57-b50b-4543-8da5-8e2bbaa874ac","resolution":{"observed_at":"2026-08-06T20:15:52.245353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.08714","last_updated":"2021-05-18T17:55:07Z","snapshot_observed_at":"2026-08-12T09:59:57.014945Z","submitted_at":"2021-05-18T17:55:07Z","title":"Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.08714","snapshot_observed_at":"2026-08-06T20:15:51.617459Z","title":"Fighting gradients with gradients: Dynamic defenses against adversarial attacks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.617459Z"},"links":{"cited_paper":"/paper/2105.08714","citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:87c8e92501b1ab50de769e6d5ac9d50f82076268f392e35cf3c5e2a0cb1ba830","observation_id":"8a9688d8-4ca3-479c-95d0-c2b774d731fc","resolution":{"observed_at":"2026-08-06T20:15:51.617459Z","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":"2026-08-06T20:15:52.220429Z","title":"Tent: Fully test-time adaptation by entropy minimization","venue":null,"work_id":"ce066c13-c7b7-420d-a4f3-10f1a9cee458","year":null},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.622002Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:b3f720cee7fed22448f2217e41b8ef0d515261e7e53c755029e90190076408f1","observation_id":"3e469019-dd61-4439-b3e5-de896ae37070","resolution":{"observed_at":"2026-08-06T20:15:52.226974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.200679Z","title":"Improving adversarial robustness requires revisiting misclassified examples","venue":null,"work_id":"e52ab269-81eb-427b-88e4-c9b3b6a4abbf","year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.626864Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:40bf2c38cc66c0bd2248afa31d896f1f6d37c3765926dc15517313f1b10f82eb","observation_id":"9b981523-acd1-4659-88cb-0d68302c7887","resolution":{"observed_at":"2026-08-06T20:15:52.207665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.183146Z","title":"Better diffusion models further improve adversarial training","venue":null,"work_id":"ab5d9c02-c32a-40ca-ab24-3ef5bfd20e74","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.631240Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:0fad3f7db2eb638df1124d1acea5c6b69d19b12bd555c066f93466fc56fab638","observation_id":"e34e9ffa-d43f-4d6a-a7bf-3b8967b6dbf0","resolution":{"observed_at":"2026-08-06T20:15:52.189073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.164214Z","title":"Towards robust person re-identification by adversarial training with dynamic attack strategy","venue":null,"work_id":"734852a5-88fb-48dc-9e89-82e9e02395eb","year":2024},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.635709Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:73ba3e5b300d283d46c883e310586a925b34307d5ec243d2051ec50100f40e42","observation_id":"9c51a591-f94b-448b-ad92-6b0d638c94f7","resolution":{"observed_at":"2026-08-06T20:15:52.170559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.146163Z","title":"Improving vaes’ robustness to adversarial attack","venue":null,"work_id":"239c4d9c-73d1-48fa-bb37-89b607422c74","year":null},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.640167Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:325df947464abc586368503e2fe1261da2ee375160e9468a496a1083475bd666","observation_id":"d58a8896-0a57-41af-86f9-0c560663eb8d","resolution":{"observed_at":"2026-08-06T20:15:52.151849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.125419Z","title":"Fast is better than free: Revisiting adversarial training","venue":null,"work_id":"929423d1-5bec-4bcd-8242-ce9440d1af7f","year":null},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.644701Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:39d71f9bde6837fabd14ae3fd8dc2c60b179fc975507b629e6ce2d5b60c1621c","observation_id":"e8fc2c2d-14a9-4284-87d3-65c635d8f364","resolution":{"observed_at":"2026-08-06T20:15:52.132243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00322","last_updated":"2022-11-01T08:18:07Z","snapshot_observed_at":"2026-08-12T10:58:23.606032Z","submitted_at":"2022-11-01T08:18:07Z","title":"DensePure: Understanding Diffusion Models towards Adversarial Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.00322","snapshot_observed_at":"2026-08-06T20:15:51.649141Z","title":"Densepure: Understanding diffusion models towards adversarial robustness","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.649141Z"},"links":{"cited_paper":"/paper/2211.00322","citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:38d4cca8c186052fbeeb009528506e7b4bfcc7dd37e9528e6807dbdf7d79320b","observation_id":"a8ec3aa8-f724-46fc-9ae3-1febff07c695","resolution":{"observed_at":"2026-08-06T20:15:51.649141Z","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":"2026-08-06T20:15:52.107674Z","title":"Spatially transformed adversarial examples","venue":null,"work_id":"525ccdd5-3ee1-4685-9483-7abe25871386","year":2018},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.653964Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:899e1634bd198228c093db481b213f189cb26e6a6cfedf30dd49a2ef5082083a","observation_id":"fb27837b-df60-47ff-8045-30d46e96ce49","resolution":{"observed_at":"2026-08-06T20:15:52.112846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.088319Z","title":"Adversarial attack against urban scene segmentation for autonomous vehicles","venue":null,"work_id":"0d2f85ce-d1ef-47e0-8b06-256a878e5cab","year":2020},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.658480Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a3d85fc07314defdcc63197c51294e9fc413ebc237a726e11413c38a0b75ba6e","observation_id":"76793e65-1732-4c76-a323-885e72168fba","resolution":{"observed_at":"2026-08-06T20:15:52.094691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.067216Z","title":"Exact adversarial attack to image captioning via structured output learning with latent variables","venue":null,"work_id":"f2d46bb1-5eb0-4187-b0ac-ad354dcc84e2","year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.663379Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:24280274cd0d2352240a747c915d8e7247064074d7f3aed59b9cce73cd762591","observation_id":"2cd1cef2-377f-4fe3-95da-9b7ee9478728","resolution":{"observed_at":"2026-08-06T20:15:52.074692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.044938Z","title":"Class-disentanglement and applications in adversarial detection and defense","venue":null,"work_id":"b3bcea62-753c-4550-9558-81828cce94e8","year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.667973Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:8c9c85d5608c980e914be3d8566043f2a54d27df452973acb80a838710d81c11","observation_id":"5c260c79-12d3-4f94-bef6-59001898c528","resolution":{"observed_at":"2026-08-06T20:15:52.051157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.025186Z","title":"Adversarial purification with the manifold hypothesis","venue":null,"work_id":"f5531967-d2e8-4561-b6bc-90c8ef8ef23b","year":2024},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.673170Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:faa68eeeb5a49707f07d7ba8bd4197089d2a1979624dd4f627867f4c0a8eeb10","observation_id":"4eb8c5f8-b83c-43ba-8115-7cc1a50b0a9d","resolution":{"observed_at":"2026-08-06T20:15:52.031458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:52.003195Z","title":"Defending against adversarial attacks using spherical sampling-based variational auto- encoder","venue":null,"work_id":"7fb43103-67f4-4904-a7c7-f7d96d9770ea","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.677654Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:d2716016933b4d883708ffb860e9eb408490cf68c411c3ffc6ab342f208e21e9","observation_id":"e66b31c4-7245-4ae7-98ab-aa4bbd609380","resolution":{"observed_at":"2026-08-06T20:15:52.010151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.982352Z","title":"Adversarial purification with score-based generative models","venue":null,"work_id":"61016214-5de2-461d-9fbd-ba7858c7d20f","year":2021},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.682209Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:9328d4dbeb43f6d54c9961b02be8fb441772eea9be832dc22e948b2f5f4b691a","observation_id":"23180848-633f-4ace-ad15-266a0e10aea0","resolution":{"observed_at":"2026-08-06T20:15:51.988809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.963503Z","title":"Automa: Towards automatic model augmentation for transferable adversarial attacks","venue":null,"work_id":"390a5e0d-ccf4-4bf4-80aa-bb8024aff248","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.686951Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:130daa0c053476851af367c0c75f6a483cccda283d43c153a881b8409ba85359","observation_id":"5aae5aa0-828b-4605-81ff-3caf547133fa","resolution":{"observed_at":"2026-08-06T20:15:51.968846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-08T14:57:17.868613Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-06T20:15:51.691529Z","title":"Wide residual networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.691529Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:83015fe8ba228d8d32d1b5ed16356678238a5b22c27b4500e4d9e60524e87ece","observation_id":"1f02afb0-398a-4730-aa88-748e6a27a04b","resolution":{"observed_at":"2026-08-06T20:15:51.691529Z","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":"2026-08-06T20:15:51.697269Z","title":"Theoretically principled trade-off between robustness and accuracy","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.697269Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:660cc30860c34cbd6f17b8943f25f90282793c8a09235be0a4c95506d468e12b","observation_id":"b7525d0b-f5ef-4781-9f17-b06bcba2a263","resolution":{"observed_at":"2026-08-06T20:15:51.697269Z","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":"2026-08-06T20:15:51.931504Z","title":"Meta invariance defense towards generalizable robustness to unknown adversarial attacks","venue":null,"work_id":"a722bfd1-447d-4652-90af-4464522bddbc","year":2024},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.702196Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:3472d2aab4f9fa20d0c967b104cb70ca8bbd2696b1e875f774c4ed710ae84554","observation_id":"fdf0e505-4846-44ee-9eed-1e30e6cc67d8","resolution":{"observed_at":"2026-08-06T20:15:51.938026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.912753Z","title":"Memo: Test time robustness via adaptation and augmentation","venue":null,"work_id":"816558f9-51dc-4b29-8627-f33ce49fe0d4","year":2022},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.706603Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:01c2b765b460d8864f5c73030cb06b8bdb7b9a10a3a4563eba942ff165a22df8","observation_id":"14137e25-2246-4cab-aa0c-f49036fa8ae6","resolution":{"observed_at":"2026-08-06T20:15:51.919478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.890762Z","title":"Detecting adversarial data by probing mul- tiple perturbations using expected perturbation score","venue":null,"work_id":"fade9333-e430-4b38-9b3f-f2c36003d3b0","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.711242Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:d963d9049640f9dc5e1a595a16939a7b4977ba119747836b0788e60efd8b9c42","observation_id":"8bee4fcc-6c85-4798-a7f6-da7794285d47","resolution":{"observed_at":"2026-08-06T20:15:51.896469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T20:15:51.872524Z","title":"Robust physical-world attacks on face recognition","venue":null,"work_id":"254b9576-4cb8-4a5c-927e-8d04e7abc478","year":2023},"citing_paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T20:15:51.715759Z"},"links":{"citing_paper":"/paper/2507.03427"},"observation_digest":"sha256:a5274664b9517957ebf40f3355d425fc2d24bd72f878a8de7af0ed3d5915442c","observation_id":"02a56ebb-8f28-42c4-b0ab-3b8a11a96c9a","resolution":{"observed_at":"2026-08-06T20:15:51.878229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.03427","last_updated":"2025-07-04T09:35:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T09:19:40.173860Z","submitted_at":"2025-07-04T09:35:01Z","title":"Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense"},"reference_resolution":{"displayed":71,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":58},"total_outbound_references":71},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2507.03427."}