{"as_of":"2026-08-10T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffd4e4d11b5273548343f0ef6cb3c0dc311876d57a36a0586c50884585df618e","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:30:44.209229Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.07225/citation-record","integrity":"/paper/2502.07225/integrity","json":"/paper/2502.07225/citation-record.json","paper":"/paper/2502.07225"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:30:44.818145Z","title":"IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI","venue":null,"work_id":"5704a44f-3701-4b99-b476-79040cf15cd7","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.025113Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:bec7ae7f435622276994faa54c51bcde75fe3e283f89979a06d469d4ffe9c375","observation_id":"671d717a-7a21-4561-956a-99868b9c2dc1","resolution":{"observed_at":"2026-08-08T13:30:44.823066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.804391Z","title":"M., and Zisserman, A","venue":null,"work_id":"07f362ce-3f20-4076-8150-9f32b5855b8b","year":2018},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.030756Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:c6bc690fc6b1bb7df3e59b75ae87b94a9264baf74223c802269070664b84256f","observation_id":"bcb789f3-07db-4d64-9a77-ca191e5b1f29","resolution":{"observed_at":"2026-08-08T13:30:44.808869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.790135Z","title":"Extracting Training Data from Diffusion Models","venue":null,"work_id":"c6e33e36-25dc-49b2-bbc6-a7532ebd2b2a","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.035490Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:ca0843025a9cd76f65bf148d77fd416541adffecb727547970d6736a2d2ff831","observation_id":"1eb87164-7478-419f-a48e-46c33ab310a4","resolution":{"observed_at":"2026-08-08T13:30:44.794910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.776902Z","title":"and Mo, J","venue":null,"work_id":"fdeba9cc-9026-4314-854c-a4512fc1acc2","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.040316Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:99c912b52367943127ee62b10e19f87f45ad97cdcad231266072c91c9a86ecac","observation_id":"0ab499b1-849e-4b01-aa8e-f2bd897227a0","resolution":{"observed_at":"2026-08-08T13:30:44.781136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.763464Z","title":"TopIQ: A Top-Down Approach from Semantics to Distortions for Image Quality Assessment","venue":null,"work_id":"85f596f5-f0b9-4e54-9e4e-51f53a6d6ece","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.045502Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:ec8c50014cc7d60870c6bb8e6ccb1b2180e280853e4ba6a4e760174fbf39f653","observation_id":"5400f1fb-7a97-4340-b60b-29546d234b4b","resolution":{"observed_at":"2026-08-08T13:30:44.767815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.749647Z","title":"ArcFace: Additive Angular Margin Loss for Deep Face Recognition","venue":null,"work_id":"dd5eda26-5be1-4028-a854-afa3112ed5b1","year":2019},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.050325Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:2f4c4123f7bcbde7d0946cad8540df05c3338fe9adc927601333a2160fce6dca","observation_id":"570326c0-9a09-4139-b4d1-8e3be14ba8ce","resolution":{"observed_at":"2026-08-08T13:30:44.754021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.735895Z","title":"RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild","venue":null,"work_id":"02379cc9-f3e4-4c87-a289-17e65ff813a7","year":2020},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.055519Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:d48366e850ce9b001ba897cf5885333faf3910ff4d29883b01d0246d3a705333","observation_id":"25c74110-9343-47af-b4e9-8e2930db416f","resolution":{"observed_at":"2026-08-08T13:30:44.740271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.722796Z","title":null,"venue":null,"work_id":"604cbdd0-ec06-4bf5-9462-0913d0360059","year":2008},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.059830Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:a0b7ea98fddb7dad58c91a88595b8e48815d9a07a12e5af5b5d74549291b1788","observation_id":"aa490617-9f01-4351-8935-70edc8801221","resolution":{"observed_at":"2026-08-08T13:30:44.726997Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.709078Z","title":"and Nichol, A","venue":null,"work_id":"2f5feac7-e49b-4227-89fc-c98ebe6d42ee","year":2021},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.064274Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:cb57affbfca7820ae54243982eb2bbacbea25fdc572fc5ce38576f5c3b9e4ad5","observation_id":"f86b5308-a8ae-40de-8db0-1728da6fdbd1","resolution":{"observed_at":"2026-08-08T13:30:44.713554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.695926Z","title":"Meet The Three Artists Behind A Landmark Lawsuit Against AI Art Generators , 2023","venue":null,"work_id":"17d6319c-65da-4f30-af30-58d74b0a4d47","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.068970Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:1ceb6fbd1f52a3d675fb5e96b513013de871c34a27dca8ad3be7cb5ff5eed8f7","observation_id":"f14ca758-1364-4763-a1bf-f1a7bab60d78","resolution":{"observed_at":"2026-08-08T13:30:44.700289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.682257Z","title":"J., Shlens, J., and Szegedy, C","venue":null,"work_id":"f9b8d6d5-73be-42e0-8a01-39f7c718729c","year":2015},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.073495Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:cce4bd7556b8e8021b819860bc4c6aa911c6d4ffa459f321d027350dca43962d","observation_id":"8e568dba-4d76-40a8-b30c-596418d3c2b3","resolution":{"observed_at":"2026-08-08T13:30:44.686736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.668500Z","title":"GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium","venue":null,"work_id":"ea922877-da16-4f1b-bd5d-78a295830467","year":2017},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.077921Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:ed0e1064fb0a8a98862993ce1b7d3c9108f256ef98413e0b20a2c16748caf740","observation_id":"57f96a6c-1dfb-4c0e-a1ae-612bbdb2a426","resolution":{"observed_at":"2026-08-08T13:30:44.673276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.651091Z","title":"Denoising Diffusion Probabilistic Models","venue":null,"work_id":"99fba022-64ca-4273-aded-112181a1eb57","year":2020},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.082475Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:cfbfee5c3485575e2663100fe7066f143011037cd7cafbcab034781721520c42","observation_id":"349252cb-fa12-4e38-81d0-291b5dcfc3d6","resolution":{"observed_at":"2026-08-08T13:30:44.655756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.637074Z","title":"Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI","venue":null,"work_id":"83e8903e-fd74-4c18-a7b9-40a846b2d70d","year":2025},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.086958Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:c64f292fc170cc211786196ff736c16595d0c362f540271d0e17042757f17e50","observation_id":"1b7088c3-0a2f-483f-b506-a2d221c56910","resolution":{"observed_at":"2026-08-08T13:30:44.641782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.623607Z","title":"J., Shen, Y., Wallis, P., Allen - Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W","venue":null,"work_id":"ca1f1cea-6dc1-4ec4-b474-29f309891d93","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.091288Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:07d43f4fb2d15097c0c0a0cb227731f8ed8a80d080134de17f98717bae38caf5","observation_id":"b418a6ae-b8ee-4b4b-850b-90542aad5748","resolution":{"observed_at":"2026-08-08T13:30:44.627941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.609892Z","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","venue":null,"work_id":"20251c5e-d7b0-4959-8e6e-56a3f8826806","year":2018},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.095919Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:54c16b1448437530908669383139297e9520c03f54e1655a892aa606badb2e7f","observation_id":"34e5f897-fbe7-4bcb-94ed-2dcff0bae00f","resolution":{"observed_at":"2026-08-08T13:30:44.614440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.596045Z","title":"Imagic: Text-Based Real Image Editing with Diffusion Models","venue":null,"work_id":"0431e807-c112-494d-a292-66d5294ee579","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.100323Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:d845bac9089d2f85094895f5c6899d257d9f41d052b2e5edecad521f025ecd45","observation_id":"10806c88-c04a-4136-9b8e-3cc0b0a75aae","resolution":{"observed_at":"2026-08-08T13:30:44.600532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.582885Z","title":"V., Phung, H., Nguyen, T","venue":null,"work_id":"034c8d83-d82c-4a70-aa73-4120ff6711b8","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.105029Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:a26d0239644264898eb26480750b78a8c95acea49fd7c9ffdc311c3ffe60dda5","observation_id":"6808ff05-3380-447f-92ef-0305a9695f37","resolution":{"observed_at":"2026-08-08T13:30:44.587065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.569092Z","title":"Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era","venue":null,"work_id":"3023284f-463b-4a16-92ed-3755f736838c","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.109298Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:cbdf60c318dd4a303b9c7fad133edf617122d542063d07817d7c8a2ab1c9f5ad","observation_id":"3536a557-ed73-41f1-8c0f-aad1f4fb1382","resolution":{"observed_at":"2026-08-08T13:30:44.573746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12683","last_updated":"2023-05-22T03:43:34Z","snapshot_observed_at":"2026-07-06T15:30:26.626858Z","submitted_at":"2023-05-22T03:43:34Z","title":"Mist: Towards Improved Adversarial Examples for Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12683","snapshot_observed_at":"2026-08-08T13:30:44.113526Z","title":"and Wu, X","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.113526Z"},"links":{"cited_paper":"/paper/2305.12683","citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:cf276e9ef2b654ce069c4ccad95410aaae0b4b9561a18c0792781914d09eec94","observation_id":"454537b1-3825-4125-82b7-8b94e0091e62","resolution":{"observed_at":"2026-08-08T13:30:44.113526Z","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":"2026-08-08T13:30:44.555145Z","title":"Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples","venue":null,"work_id":"c6c8c1a4-3f9d-4704-8421-c312825cfc4c","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.118404Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:201af6d99977ee4d28ef8bf3d7fbaa0a340efaf403a3399fe118dd1954b9f475","observation_id":"8ec43f6c-cb13-4e1a-9a04-ce1477c4da12","resolution":{"observed_at":"2026-08-08T13:30:44.559907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.541320Z","title":"MetaCloak: Preventing Unauthorized Subject-Driven Text-to-Image Diffusion-Based Synthesis via Meta-Learning","venue":null,"work_id":"414f6aa9-a4cb-4c76-9b73-24b90f09cc9c","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.122563Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:30ffe776495fc7cda8d0451faedf49fc42a733c5f741a188fef9eacc6947552a","observation_id":"5c0dd4f1-279a-4f7f-ac2a-66a6d8971630","resolution":{"observed_at":"2026-08-08T13:30:44.545872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.527697Z","title":null,"venue":null,"work_id":"d3518979-866e-4276-8cc3-96a8a7c290b8","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.126919Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:980680fcc9d48b9a1762553f8aa2275ebd6ad4f4bccfe9d93d64e073101c5af2","observation_id":"772797be-0f33-4962-bd24-acfb6f17f953","resolution":{"observed_at":"2026-08-08T13:30:44.531984Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.514360Z","title":null,"venue":null,"work_id":"dd1d1706-dc8f-4394-8311-04f3c89f3896","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.131521Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:66dd3038e28dae7737f63727946c4ae14266bfab0166519b92ac7ef8ad9fca0b","observation_id":"b475f80b-12ea-4ab6-84e3-d6d14a315836","resolution":{"observed_at":"2026-08-08T13:30:44.518768Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.500242Z","title":null,"venue":null,"work_id":"09ec8c7e-306a-4984-9da6-03313a7ccd80","year":2018},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.136188Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:51594572ab84d0ad0093a55ad5be120fb6c49e245aa5b56655f46d943129905f","observation_id":"aa5cc02e-7035-41a1-8581-01accff1a781","resolution":{"observed_at":"2026-08-08T13:30:44.504762Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-08T13:30:44.140570Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.140570Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:b58435f72fc8f0f63cb1053b8203e72b0ef0e21a704513ffa19995cde5e45069","observation_id":"2621801b-e521-425a-bc12-dd698525ac5b","resolution":{"observed_at":"2026-08-08T13:30:44.140570Z","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":"2026-08-08T13:30:44.486042Z","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","venue":null,"work_id":"fa5ebc8c-14ae-4fad-b853-25b207dc2f96","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.144854Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:215f16524a7bc779af099166eb03c68ba79722d06a45146b377ab4032fead8b3","observation_id":"a2d0682a-2e7a-48f5-83e2-e772948fdfc6","resolution":{"observed_at":"2026-08-08T13:30:44.490737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.472092Z","title":"Diffusion Models for Adversarial Purification","venue":null,"work_id":"8f658be5-f55d-4fa9-ac68-b2757df09d53","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.149182Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:5951b62568bf734a4d0a9ddd6c20580a0291da1e67ac8742dd54cc5084db5730","observation_id":"ff5a4ec6-96f4-45fb-ab1e-2b6695bf95a6","resolution":{"observed_at":"2026-08-08T13:30:44.476725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.458021Z","title":"Protective Perturbations Against Unauthorized Data Usage in Diffusion-Based Image Generation","venue":null,"work_id":"c9683db7-1e27-41b1-96e9-a41c492020ed","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.153890Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:3d112cfc1b6dd59b7e493ab2c37a542797899037a545be6cf8c3e2bb07571085","observation_id":"2588e70a-d48b-44ec-88a9-22567ebd2f65","resolution":{"observed_at":"2026-08-08T13:30:44.462975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.444036Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models","venue":null,"work_id":"42a53271-d2b6-49b4-904d-b5e3054f49e9","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.158158Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:15f8a1f60405e46e68fbedc3f66f7f60e2d43a3198f3a05dff8ae712a7144317","observation_id":"5580d35b-f959-45d8-8a58-584602813e7b","resolution":{"observed_at":"2026-08-08T13:30:44.448482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.430203Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","venue":null,"work_id":"3a64e7fa-5528-4ebc-a482-b6ac8567a188","year":2015},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.162321Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:dfc543b79da5004b7458d0ae0515835baeedda3f9c73472bcc3433e785441ffd","observation_id":"745334b0-cd76-44bb-a497-a04b0a9efc69","resolution":{"observed_at":"2026-08-08T13:30:44.434733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.415774Z","title":"DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation","venue":null,"work_id":"66fa8e6d-336b-4fa2-b056-5c4df08a4740","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.166689Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:832a94d447c22eab7faec735c2bde4c3d2b2c8cc01d42c7f75215362cce671f1","observation_id":"f977dc2b-043b-4a4b-994b-036bb1f6a3cb","resolution":{"observed_at":"2026-08-08T13:30:44.420330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.402040Z","title":"A., Ho, J., Salimans, T., Fleet, D","venue":null,"work_id":"a2490fb1-e235-4100-a750-678d444dba44","year":2022},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.170768Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:c14934c297fa7e9a5767d09389b042587bb984ce8dfd8c9be2f7d1fe0e2ac830","observation_id":"884e384f-36f7-4039-8558-80d902d7b7a4","resolution":{"observed_at":"2026-08-08T13:30:44.406362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.387385Z","title":"Raising the Cost of Malicious AI -Powered Image Editing","venue":null,"work_id":"c0455ace-4fbd-44e1-9c37-5ea488f0b768","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.174924Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:e33e2c14ca4d2711f96f0e4807edf2b62fdcfa80e2a374ff820f3f6918b91cbb","observation_id":"46d7b026-42e2-4a4e-a0c5-b2fab5eb5839","resolution":{"observed_at":"2026-08-08T13:30:44.392118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.372582Z","title":"D., Croce, F., and Hein, M","venue":null,"work_id":"7d6bc259-ac20-4e55-8dd1-8a2b362d1c47","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.179064Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:4ff2a4be32e85e7d5232517cff00942dd97f22042cb508cd1a740beb44cd2566","observation_id":"38141475-4d10-48c1-8c2d-76e4fdbd7d70","resolution":{"observed_at":"2026-08-08T13:30:44.377015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.356934Z","title":null,"venue":null,"work_id":"d192679e-0548-4850-9149-2ebd8496bdc6","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.183392Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:422b739c59546aaf0a1e149fd597130bc96bcd63ba8bc0df95b3b9fe53ab5bb3","observation_id":"ddbc6d77-31ab-4595-9a4b-960c85faa7c4","resolution":{"observed_at":"2026-08-08T13:30:44.362050Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.341277Z","title":"Stability AI Image Models — Stability AI , 2024","venue":null,"work_id":"93aec98f-5dc4-4e61-bc99-156105d8ca5d","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.187767Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:3c0f5ee0b0ed9147adf312ae0989872aff71af718479fddd539457a02b39c3fd","observation_id":"719ad098-bb9b-43cd-92e7-ad1b263b587e","resolution":{"observed_at":"2026-08-08T13:30:44.346256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.326524Z","title":null,"venue":null,"work_id":"e4833242-ab86-4dd1-80ad-c3f979538a78","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.191949Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:23a2b8b32597f2cce009af4d5dece49f40b2ada0b1d778a382b5aa52504ee503","observation_id":"d2b34c05-d90a-4215-a3de-d4bca34ef6ba","resolution":{"observed_at":"2026-08-08T13:30:44.331211Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.311733Z","title":"Toward Effective Protection Against Diffusion-Based Mimicry Through Score Distillation","venue":null,"work_id":"6b57e02d-f1e3-4142-9391-07045d950fc9","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.196241Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:28c2a987cba3fda0e7eadd3adc112f2c04f0efd2f9ac9d111afd945660029b24","observation_id":"09d90159-aaa8-4c08-9f28-da66208d6b9e","resolution":{"observed_at":"2026-08-08T13:30:44.316454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.296481Z","title":"Inversion-Based Style Transfer with Diffusion Models","venue":null,"work_id":"f355baa9-c6e7-422f-ac14-8632ad3aaff8","year":2023},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.200831Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:b5aa33daa378703ed45900cf57ada3cf8f56064c555c6fd0be2c804743236aac","observation_id":"e2baa408-fb5b-4b07-afc0-37cedc01aca7","resolution":{"observed_at":"2026-08-08T13:30:44.301973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.280686Z","title":null,"venue":null,"work_id":"c0ef71be-205b-4790-b329-e349959ccdb4","year":2024},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.205151Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:d7ddaa6c974a417b85ac0e50b1f917ce0758916e4919bb204e7a2cd002cf1406","observation_id":"7f2646ee-9324-4ccc-aab4-abfbd751acc5","resolution":{"observed_at":"2026-08-08T13:30:44.286272Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:30:44.209229Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-08T13:30:44.209229Z"},"links":{"citing_paper":"/paper/2502.07225"},"observation_digest":"sha256:93248d8203620e8921df47c2a2845c9b4eaaaf5ea39095be9e6b6a6a251c709c","observation_id":"66417569-b7bb-4359-addf-ec92733b0f33","resolution":{"observed_at":"2026-08-08T13:30:44.209229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.07225","last_updated":"2025-06-16T07:12:59Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T09:31:32.075567Z","submitted_at":"2025-02-11T03:35:35Z","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":32},"total_outbound_references":42},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2502.07225."}