{"as_of":"2026-08-14T02:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1506e3c827337e2a2471fd1fde0397cef43d3c7cad04c94d63335bc5aab33880","coverage":[{"denominator":89,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":89,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:01:26.982853Z","state":"measured"},{"denominator":89,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":89,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2412.07274/citation-record","integrity":"/paper/2412.07274/integrity","json":"/paper/2412.07274/citation-record.json","paper":"/paper/2412.07274"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:01:26.585798Z","title":"Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.585798Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:6f2f520a71cad347767543d07ac66929fa17c7c0f2d9c034075494ae78d34532","observation_id":"7c771da7-58ed-4532-92cb-458de2a3154b","resolution":{"observed_at":"2026-08-11T19:01:26.585798Z","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":"2026-08-11T19:01:26.590587Z","title":"Explaining and harnessing adversarial examples,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.590587Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:38f93bc3c1d3fb6336069838412d7975b6cb8d23d96f808f782d78bafc529ba1","observation_id":"6d100a05-138d-483e-9d83-e14233475462","resolution":{"observed_at":"2026-08-11T19:01:26.590587Z","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":"2026-08-11T19:01:26.594985Z","title":"On the robustness of semantic segmentation models to adversarial attacks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.594985Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:3ea743b0b6faf8d8233f1e72a827b7ae3cada49567060bff19939adfae87d789","observation_id":"8e40a2bd-0dd6-4f12-a08d-d9c53cddab23","resolution":{"observed_at":"2026-08-11T19:01:26.594985Z","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":"2026-08-11T19:01:26.599662Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.599662Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:efb8bf401fb5bd388c3344543f8e303c05ab18f34c43c6ded6cd3c7bd1d5f822","observation_id":"cf985e39-fb19-4ab7-a40e-9adf86e1b6f4","resolution":{"observed_at":"2026-08-11T19:01:26.599662Z","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":"2026-08-11T19:01:26.604336Z","title":"Practical black-box attacks against machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.604336Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:9c88d7ec2844bfbf22c704008a5bc6dee779d5e8aed04992e166cadb030b00ec","observation_id":"a729746f-b54b-42f9-9de4-c364caf95ee6","resolution":{"observed_at":"2026-08-11T19:01:26.604336Z","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":"2026-08-11T19:01:26.609111Z","title":"Dast: Data-free substitute training for adversarial attacks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.609111Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:f13427ba5f85610dc6fa3944193c2998f1e696ccabf4796c0db6dec0ec93901e","observation_id":"8fd15976-83e1-44e7-8cca-89f9f4c28eaf","resolution":{"observed_at":"2026-08-11T19:01:26.609111Z","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":"2026-08-11T19:01:26.614048Z","title":"Square attack: a query-efficient black-box adversarial attack via random search,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.614048Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:bd5c516a7a94e28ac14f49c53f3657827ca94c2be76359c39eaed489c51bb60f","observation_id":"5bfef3c4-b990-4a69-b541-28c5bc3fe733","resolution":{"observed_at":"2026-08-11T19:01:26.614048Z","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":"2026-08-11T19:01:26.619010Z","title":"Simple black-box adversarial attacks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.619010Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:5e581645b0c5eb487a6aec9d19fbd1a371c4de707d1337957ef0c25a1ffedc61","observation_id":"63d0aabc-ba1a-44bc-b2b5-9096f7040243","resolution":{"observed_at":"2026-08-11T19:01:26.619010Z","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":"2026-08-11T19:01:26.623284Z","title":"Sign bits are all you need for black- box attacks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.623284Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c6aedbc6ed600e3e1221654c43c82a95ba2b9690c707f1f19bea265ba1507743","observation_id":"a014bb5d-4ffc-492d-8546-a4c91fbe515e","resolution":{"observed_at":"2026-08-11T19:01:26.623284Z","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":"2026-08-11T19:01:26.627518Z","title":"Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.627518Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:34f83c8d2979c58163aaafaa0c25510523815892b3088666aab4bb1755cbcea0","observation_id":"b6169289-c489-4733-bdaa-7c3b2d480bbd","resolution":{"observed_at":"2026-08-11T19:01:26.627518Z","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":"2026-08-11T19:01:28.238347Z","title":"Black-box adversarial at- tacks with limited queries and information,","venue":null,"work_id":"400cb2b3-d856-488f-a43d-41e0e93c1b7c","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.631840Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:caa8b035f989fbcb3c9571a4afb98bdf6a8c07005764ed7015f1afdb69f47665","observation_id":"7fd2032e-a8a9-45a3-92c9-53c72846246b","resolution":{"observed_at":"2026-08-11T19:01:28.244362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.223364Z","title":"Adversarial risk and the dangers of evaluating against weak attacks,","venue":null,"work_id":"856ad2df-ab0e-412c-90ea-fbbadbfa2ab4","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.636413Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:b4bba448f1d354aa07c6e3404b224be28bfa0848649c7354cbf15aaab7aeb136","observation_id":"4e37f5e3-993b-4dd3-b382-75eeb0e75c44","resolution":{"observed_at":"2026-08-11T19:01:28.228319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.207199Z","title":"Adversarial examples for semantic segmentation and object detection,","venue":null,"work_id":"eb8e1820-9aa1-4e98-a7d8-c42d0e619bd0","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.640783Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:27dc127da0cd69ada625d2947af2b905204bd59c29f14249e8e26499c467ced0","observation_id":"c5f87bfa-37d8-4e5a-8eb3-b4ab8f42d1b8","resolution":{"observed_at":"2026-08-11T19:01:28.212777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.191639Z","title":"Skip connec- tions matter: On the transferability of adversarial examples generated with resnets,","venue":null,"work_id":"985d852e-3fe5-45fd-9716-a01ca2da5b9c","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.645137Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:5fb8a4993e17b1a80b2875bafcaf8fa0c3de1c2264172c29824a31c224652b1f","observation_id":"09d7feb2-4545-4169-af43-b3c02556af94","resolution":{"observed_at":"2026-08-11T19:01:28.196269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.176704Z","title":"Gen- erating transferable adversarial examples against vision transformers,","venue":null,"work_id":"83b5bae6-06c4-4a54-be71-365d3dfd77f4","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.649281Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:96f8afb16ea88106566c02ffe5640fbfc92497ea393b8dcf7905cbfb8f223ebb","observation_id":"0b09827b-b3be-42bd-8573-2bc0898abc7a","resolution":{"observed_at":"2026-08-11T19:01:28.181466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.162052Z","title":"Diverse generative perturbations on attention space for transferable adversarial attacks,","venue":null,"work_id":"209a238c-079d-45fa-80bb-8fe49baaeee2","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.653415Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:2da3ff97afbac10915770832836bc335b8fb8efd6c22e6a7ceedb44edb0e2246","observation_id":"17f6becb-d5c2-4d70-86c3-30317080ff40","resolution":{"observed_at":"2026-08-11T19:01:28.167190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.147404Z","title":"Transferable adversarial attacks on vision transformers with token gradient regularization,","venue":null,"work_id":"fe58d109-37ee-4aaa-90cb-e4c21b5d3a35","year":2023},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.658068Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:a10df64910679e1680150834f2fda8eba99b3bab9bf681d260726e2787c41b06","observation_id":"a77e96a1-3ae1-4f65-a8b2-f51a7ed84eaf","resolution":{"observed_at":"2026-08-11T19:01:28.152153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.132674Z","title":"Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients,","venue":null,"work_id":"153ddd46-615a-471a-8b9e-4b493f031ad4","year":2023},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.662230Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:aebe2c4445b13f381f507ccac499428737b5cc22bb802c4d927fc7fd6e9193a9","observation_id":"dd9effa3-ce0b-47fc-96bb-949931637901","resolution":{"observed_at":"2026-08-11T19:01:28.137503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.117216Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics,","venue":null,"work_id":"45fcae8f-64cf-4493-a5c8-d4fbe1050dba","year":2015},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.666575Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:3868e9a985176dd1f4a3bc88817c09ff039eb85d9cfdbd36731d64dba62162c8","observation_id":"05c9f60c-39f0-43ea-8eb5-1122d8529c7b","resolution":{"observed_at":"2026-08-11T19:01:28.122093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.101530Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":"2078934f-e1fb-4442-88be-b362b00b117b","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.670708Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:b467f082fddd607ceac257b6c060428fb9c9297f21df9352d067465563f7ded1","observation_id":"af7b9ac9-ff96-4cdc-aeba-abf07edeaa02","resolution":{"observed_at":"2026-08-11T19:01:28.106710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.081413Z","title":"Generative modeling by estimating gradients of the data distribution,","venue":null,"work_id":"60112358-d555-4750-bced-b8912587e89b","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.675007Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:7b725b1986b421145204c601707f36fd19dcc8d3e34fd550b80823fd6e5be172","observation_id":"5dfc8248-16b7-4099-9873-ff1959255878","resolution":{"observed_at":"2026-08-11T19:01:28.086615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.064253Z","title":"Rethinking adversarial transferability from a data distribution perspective,","venue":null,"work_id":"0dab3798-9126-48ab-9e47-b75d675f0607","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.679561Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:73d43f2c9e3adde9bf5132523a46845c7eddceef2cf4dc53559f6a936d8f864f","observation_id":"cd6e8bc2-2827-4583-a2aa-59e745a7cdac","resolution":{"observed_at":"2026-08-11T19:01:28.069574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.047854Z","title":"Advdiffuser: Natural adversarial example synthesis with diffusion models,","venue":null,"work_id":"1b6f9c08-f3ef-490b-aa01-851f8203b456","year":2023},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.684735Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:2729a26031aaae78e98886fec16642dc7577ef20ff3be6c5339d7724e4706839","observation_id":"02770ac3-bcef-4150-bb29-2a01c25f3fd0","resolution":{"observed_at":"2026-08-11T19:01:28.052676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.032282Z","title":"Revisiting graph adversarial attack and defense from a data distribution perspective,","venue":null,"work_id":"2a1c57cd-3574-4489-a3bc-effd460da173","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.689092Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:93c9d1805028bad6deee93f9909178b2b47e644c769cb3d97095020f9548e07d","observation_id":"d8daaa0b-6f61-42ad-b10e-4573d408a6c3","resolution":{"observed_at":"2026-08-11T19:01:28.037390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.017313Z","title":"Robust evaluation of diffusion-based adversarial purification,","venue":null,"work_id":"836955ec-d115-40cd-a71f-9afbf021dd30","year":2023},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.693652Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:d10b19b1166bcc253f819edb64bd1a55bb1fd13602def2c1f020df6323be4d1f","observation_id":"d99a08e7-d0ac-4c09-b3c3-08b9539482b1","resolution":{"observed_at":"2026-08-11T19:01:28.022218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:28.002829Z","title":"Diffusion models for adversarial purification,","venue":null,"work_id":"195ef97f-4acd-4f26-acaf-99a53329eb76","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.697842Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:634d8d1f3217287269c0cfd616342fb70e3d67e3586aab238d2dceb31cf5d840","observation_id":"ffd7a169-c0e3-4279-8340-61f9b24209da","resolution":{"observed_at":"2026-08-11T19:01:28.007477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14969","last_updated":"2022-06-29T02:42:05Z","snapshot_observed_at":"2026-08-13T15:34:13.869232Z","submitted_at":"2022-05-30T10:11:15Z","title":"Guided Diffusion Model for Adversarial Purification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14969","snapshot_observed_at":"2026-08-11T19:01:26.702052Z","title":"Guided diffusion model for adversarial purification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.702052Z"},"links":{"cited_paper":"/paper/2205.14969","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:d1b343894828faf2be6c6561ab0493154dcc8ab498616468b7e2558c427e41dc","observation_id":"85180841-b7cd-4a4c-83c8-8b6c7def1f34","resolution":{"observed_at":"2026-08-11T19:01:26.702052Z","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":"2026-08-11T19:01:27.988239Z","title":"Grad-cam: Visual explanations from deep networks via 11 gradient-based localization,","venue":null,"work_id":"0a3ee59c-154a-4e83-9600-c2e292991432","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.706972Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c0651ca1493855d621428823542180b930674a9eb2f17208e94e332755d1a1e7","observation_id":"906afce9-ed55-4bbc-bcb2-ad2e2b34c602","resolution":{"observed_at":"2026-08-11T19:01:27.992987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:26.711556Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.711556Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:47d1750b0db21bbcf27892df27410653490817640d7eb249c7280643279b2fab","observation_id":"81d8e996-a2ee-42be-9bb7-e6a8b030d792","resolution":{"observed_at":"2026-08-11T19:01:26.711556Z","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":"2026-08-11T19:01:27.964185Z","title":"Towards evaluating the robustness of neural networks,","venue":null,"work_id":"db697483-86fa-4cb0-a2d2-b37b7497bd35","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.716205Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:16a7ddc1e438d9287529a3140e3e3eff12f7fc9bcaba1443d1f226146dad6ecb","observation_id":"ec32aad4-c411-4d6b-a48c-8a9acd4725db","resolution":{"observed_at":"2026-08-11T19:01:27.968845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.948848Z","title":"Impact of adversarial examples on deep learning models for biomedical image segmentation,","venue":null,"work_id":"44d4cd0d-612c-4bc9-bb81-9a616360d3ee","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.720592Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:0638bf1859b7fc5123cceda66875735ab0efc3de846dc18808b8c623d8958e9d","observation_id":"fb38a8c6-ad04-40d4-8334-d2be1978ddcf","resolution":{"observed_at":"2026-08-11T19:01:27.953800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.932917Z","title":"Fashion-guided adversarial attack on person segmentation,","venue":null,"work_id":"7f76c56d-dce3-411d-b071-586685c3241c","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.725199Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:8bd23d9370ded691f495f5caf6fefca53e55cbf9a7760bbaf7d8f74898490804","observation_id":"d60e86ca-e173-4429-a433-8b6ed9e7d55f","resolution":{"observed_at":"2026-08-11T19:01:27.937753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.916470Z","title":"Universal adversarial perturbations against semantic image segmenta- tion,","venue":null,"work_id":"21c0bb80-a780-43ad-9fa6-f628b51fd4ed","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.729642Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c94c3bc6d405b162b1af0038d86dd5b36845dbe5ef70ac0d51720608264c9ef0","observation_id":"d890328a-9eb8-4479-8f71-a8f36dd164ba","resolution":{"observed_at":"2026-08-11T19:01:27.921784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.900806Z","title":"Data-free universal adversarial perturbation and black-box attack,","venue":null,"work_id":"3301c4df-40be-4c38-bccc-e581338ec1cc","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.733959Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:3d430ac72a59e74d4b2e87497709e068d10f6a68e78177328e34e46f98c33d9e","observation_id":"7d981086-e283-4be4-8dfb-98cda3d52bfd","resolution":{"observed_at":"2026-08-11T19:01:27.905755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.886452Z","title":"Query-based black-box attack against medical image segmentation model,","venue":null,"work_id":"9a65a518-5fb3-4489-b52d-2190efce082e","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.738191Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:6d184ed3a7c927a4e674a85de8f350359e9932cf87f34ce75f73ea09e66dac66","observation_id":"caad3714-23b0-4cd3-911d-5fd1052712f9","resolution":{"observed_at":"2026-08-11T19:01:27.891096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.871690Z","title":"Surfree: a fast surrogate- free black-box attack,","venue":null,"work_id":"71f15686-c5f7-4eb3-a9ac-ff1ce766983b","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.742769Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:11c2e4b2217494af88013831423468c495fcfe954482d354b487062bc233361b","observation_id":"e1a7dcb7-e7da-4660-a157-ec5532daa269","resolution":{"observed_at":"2026-08-11T19:01:27.876461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.856481Z","title":"Simulating unknown target models for query-efficient black-box attacks,","venue":null,"work_id":"ca769386-eeb4-4429-8e07-74e0dbc462c8","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.747656Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:bba5e139360dbfe753e1c485cac199c9301287984d5fda575aba20a028e8560d","observation_id":"2a4c5f08-e29e-428b-a73e-4c216d323cfd","resolution":{"observed_at":"2026-08-11T19:01:27.861280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.839761Z","title":"Diversity can be transferred: Output diversification for white-and black-box attacks,","venue":null,"work_id":"270f26de-a327-4514-959e-7cc680544ba8","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.752643Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:5b4d19c76718ecefaf257fd58f8e742e59b93c4f8ea9388103ade87691d7803c","observation_id":"b06b975f-7893-4dcb-9b95-1e4f2c1c9f2e","resolution":{"observed_at":"2026-08-11T19:01:27.845613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.823707Z","title":"Geoda: a geometric framework for black-box adversarial attacks,","venue":null,"work_id":"a608354b-a987-4445-b10a-de8269463211","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.757160Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:2ad16649cdaf5279dd0c7bc773497f65d940fe8b51f2cc18c936d1fd305abddc","observation_id":"f129f5d5-8f42-4460-a153-9bdf19183adc","resolution":{"observed_at":"2026-08-11T19:01:27.829571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.806508Z","title":"Boosting black-box attack with partially transferred conditional adversarial distribution,","venue":null,"work_id":"3e161eb3-a88a-4010-989e-df20ca6f6c37","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.762006Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:6e0be612421411aac84cc89e26f7f914e744c63648a26a28665e6c726bde8de1","observation_id":"10671a85-89d2-4488-86d6-aa2b92621920","resolution":{"observed_at":"2026-08-11T19:01:27.812417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.790976Z","title":"Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,","venue":null,"work_id":"6f74e865-b7c9-43c6-b3a7-bff24a3e0d1f","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.766230Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:b005b241fbc38e66b630cff63f8131acce4e61693588fc4ec73050c4d0eb3edc","observation_id":"047bd209-0de2-4945-96df-48869570969a","resolution":{"observed_at":"2026-08-11T19:01:27.796753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.776118Z","title":"Parallel rectangle flip attack: A query-based black-box attack against object detection,","venue":null,"work_id":"91e03edf-57b2-4d0d-9513-23472bb696fa","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.770577Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:de81c1a080e433f7ea9a83b985402cc1cac60e3cee9d22c090f6efd7357bf054","observation_id":"e92837dd-6aec-4dc4-bf20-3ac48e32977a","resolution":{"observed_at":"2026-08-11T19:01:27.780914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.761407Z","title":"Boost- ing the transferability of adversarial attacks with reverse adversarial perturbation,","venue":null,"work_id":"63e02d8d-0ecf-4f51-a416-d254aeee5965","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.775599Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:eeaef582c4653e551d664f5864a5af95648845817788effcbf651b97b9b7c2e9","observation_id":"e74ecff1-95d8-47e9-b4b5-bc4710b7b680","resolution":{"observed_at":"2026-08-11T19:01:27.766394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.746041Z","title":"Frequency domain model augmentation for adversarial attack,","venue":null,"work_id":"f502692b-1654-4625-b3b6-335143e9d888","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.780069Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:868c69e5870ec683bd8e6b688d00a67dd5a20428bcd97586df8c66f14a70dae0","observation_id":"b91394f2-8b95-43dd-9ddb-48aa394a7781","resolution":{"observed_at":"2026-08-11T19:01:27.750919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.730696Z","title":"Transferable adversarial perturbations,","venue":null,"work_id":"33448073-aef6-4652-ab68-50cad64ab5cb","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.784423Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:eee119a14f689395b12a525961561361c5487895d004f4e03afcdd2f6c10c966","observation_id":"dc6a18d6-07d9-4c3c-932a-383d1f777853","resolution":{"observed_at":"2026-08-11T19:01:27.735599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.715946Z","title":"Fda: Feature disruptive attack,","venue":null,"work_id":"6ddff856-4498-4f72-aec5-feea7adc9ce1","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.788836Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:2efc32357d8bf0aaacd185dbc569b128b6e18029886b90e491d64f8c185f1bfa","observation_id":"c3be5989-285c-4703-b162-57b90c0f80b2","resolution":{"observed_at":"2026-08-11T19:01:27.720612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.701143Z","title":"Feature importance-aware transferable adversarial attacks,","venue":null,"work_id":"16a98424-b7c5-473c-a7d5-2887565909b7","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.793244Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:9f14af82e58d9e7e3875d621395debd97b565d186e1f534a861ccba965eb117e","observation_id":"3b747e99-b03f-4841-9ccb-ac918779e6f5","resolution":{"observed_at":"2026-08-11T19:01:27.705653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.685414Z","title":"Boosting the transferability of adversarial samples via attention,","venue":null,"work_id":"e2ef1e49-1a61-418b-aa00-f9d66686c508","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.797666Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:bcf738213e3f18329d8adbdbd77b07094d2a6952d07448fade333810e5b22c6e","observation_id":"ded3ae18-6e85-4339-b7ad-45ecdc52a780","resolution":{"observed_at":"2026-08-11T19:01:27.691231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.669426Z","title":"Nesterov accelerated gradient and scale invariance for adversarial attacks,","venue":null,"work_id":"a8f123c8-1aa8-4878-bbda-bc10d20e628e","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.802304Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:e7ebaaaaa1a9dfc2a069e139a1c8e3ebac553333ea7b6ca8b4061143929698a6","observation_id":"c8d23e42-bd5b-4527-b45c-cb05f05063ea","resolution":{"observed_at":"2026-08-11T19:01:27.674648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.654900Z","title":"Enhancing the transferability of adversarial attacks through variance tuning,","venue":null,"work_id":"71c7a399-aa34-4062-8635-9490350f40d1","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.806455Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c7ddc9834b4edbe2eeb653662413a1f34e59a234c921786acf9f8cc9d8a182e7","observation_id":"36766b4d-c468-4dfa-9df2-17deb8336341","resolution":{"observed_at":"2026-08-11T19:01:27.659772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.639144Z","title":"Stochastic variance reduced ensemble adversarial attack for boosting the adver- sarial transferability,","venue":null,"work_id":"cf263422-0701-4ddb-b8fe-ffb26876cd55","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.810737Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c81464aa8c449f60de852cee8b8ee1dad9ff8fe50fb2f61be1a13be77c17d610","observation_id":"a2d88488-4c3e-4e66-b8e1-66c0eb65bc7e","resolution":{"observed_at":"2026-08-11T19:01:27.644887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.624216Z","title":"Toward understanding and boosting adversarial trans- ferability from a distribution perspective,","venue":null,"work_id":"b9e80d91-9297-4fa7-97ba-f122b13df778","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.815443Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:1b0b50c6dd9ffdbb9b0b8494f8691db82fc7a44003ef74a9c017b75a59766134","observation_id":"ba8df23d-4934-44ca-85d2-febe4f2042c4","resolution":{"observed_at":"2026-08-11T19:01:27.629063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.609434Z","title":"Admix: Enhancing the transfer- ability of adversarial attacks,","venue":null,"work_id":"029fd317-9360-4007-b0bd-78a1c74099ff","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.819673Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:7ab0dbff0863277fa19fa7caa6efb78e4f567266ddc30c5361a5298b1dd76dec","observation_id":"9f719f52-03db-4f96-b9a2-20f6b38e268e","resolution":{"observed_at":"2026-08-11T19:01:27.614231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.594083Z","title":"Evading defenses to transferable adversarial examples by translation-invariant attacks,","venue":null,"work_id":"4de4ea23-8331-46b3-aff8-2a9025d2f965","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.823858Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:a496acb4d19d0741905ef03501687f0ab72587ebdf6ae083aa6888a457b059f2","observation_id":"3c986c88-bacd-482f-94e7-a5160125da98","resolution":{"observed_at":"2026-08-11T19:01:27.599130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.578744Z","title":"Improving the transferability of adversarial samples with adversarial transformations,","venue":null,"work_id":"c9e1bd67-23c7-41c4-b5a9-b2bb15702be7","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.828201Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:3a3caf1769791c340a80539b3990b599e669a3da25db96a889bd25de64a31d02","observation_id":"8c146337-8534-4da4-99c4-ea05d12ed033","resolution":{"observed_at":"2026-08-11T19:01:27.583517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.561866Z","title":"Diffusion-based adversarial sample generation for improved stealthiness and controllability,","venue":null,"work_id":"400a6553-75fe-433d-a270-f031ebc85180","year":2024},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.832901Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:31a15d02f70e4758cf0c55ec79f5f34a5b386c050aee1af37dd1109e29bb5af9","observation_id":"0625bc95-e130-497e-9970-6bcf0029324f","resolution":{"observed_at":"2026-08-11T19:01:27.567519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08192","last_updated":"2023-11-30T14:40:54Z","snapshot_observed_at":"2026-08-13T11:43:02.108040Z","submitted_at":"2023-05-14T16:02:36Z","title":"Diffusion Models for Imperceptible and Transferable Adversarial Attack","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08192","snapshot_observed_at":"2026-08-11T19:01:26.837294Z","title":"Diffusion models for imperceptible and transferable adversarial attack,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.837294Z"},"links":{"cited_paper":"/paper/2305.08192","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:2ac6a1493e80b6e29233d48ab75ef0517de415c34bdd2bd5d014dceb1b352c57","observation_id":"b8ce2616-bd93-44ee-9af6-93252da9db0f","resolution":{"observed_at":"2026-08-11T19:01:26.837294Z","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":"2026-08-11T19:01:27.544992Z","title":"Classifier-free diffusion guidance,","venue":null,"work_id":"40eb49e0-6b1e-489e-a4d6-26aadebcb36e","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.842112Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:27a8d452faee62375fbcdc57c0b4a059544fea54021a44e62e3a56e723d7b1b6","observation_id":"6b5057aa-ebcf-42ac-b8c0-3d182b00920f","resolution":{"observed_at":"2026-08-11T19:01:27.550615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.529364Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":"cb63cb5c-64dc-4696-a605-7362ee53dd63","year":2014},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.846678Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:f25c2c0bca780636c99c1ad33891b9e2de979e5235004d62934aba0773a7554d","observation_id":"38c2adc0-6ce1-4a80-8ccb-31808fa819f7","resolution":{"observed_at":"2026-08-11T19:01:27.534194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.514382Z","title":"Variational inference with normalizing flows,","venue":null,"work_id":"4e3fad8b-c740-45a0-b27c-b446cc1b2c1e","year":2015},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.851990Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:b8d80ee77f402f0b00b96912aa6506fadd93b70c5d3a747c12afbad3720efa86","observation_id":"273176bf-d445-414e-b3c1-beff570f974a","resolution":{"observed_at":"2026-08-11T19:01:27.519257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-11T19:01:26.856514Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.856514Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:77e75214f8597a8f22968b993de0cfd47a5d11315d312c94523f384ee13e4e5a","observation_id":"0cd274dc-5873-4298-9ebb-daf8247f38a3","resolution":{"observed_at":"2026-08-11T19:01:26.856514Z","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":"2026-08-11T19:01:27.498387Z","title":"Camouflaged object detection,","venue":null,"work_id":"7f2aee6b-9bd1-45e2-bb00-4523c90b8e9b","year":2020},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.861209Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:15dc5862cdec32bfb455fdd5828aa4ec63bea5b2ce9c7c1a07138abbbc3639de","observation_id":"eef3de05-6dc8-437b-94c0-2ca25a555a30","resolution":{"observed_at":"2026-08-11T19:01:27.503280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.480763Z","title":"Simultaneously localize, segment and rank the camouflaged objects,","venue":null,"work_id":"10d08bc8-39cd-4321-a2cc-0bf468b6365f","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.866391Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:c40cb2400725e4975f810a79b83edce165720d33a7c83e030e0b3513233c8adf","observation_id":"cd065586-5e4f-4387-aca1-78790a7694ce","resolution":{"observed_at":"2026-08-11T19:01:27.486392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:26.870802Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.870802Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:57f5c7f62d234082e6f8b2655561d8ae9b9d3c091a7fe8908d6e6ccc034a9c71","observation_id":"cc0cc517-1de6-4877-af7a-bff5fae4a526","resolution":{"observed_at":"2026-08-11T19:01:26.870802Z","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":"2026-08-11T19:01:27.452211Z","title":"Vision transformers for dense prediction,","venue":null,"work_id":"32b6c43a-c35b-40da-bedf-e05456ae6f49","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.875309Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:f1c8cc82cb989eceb5489e86dce9d6412f136d334a5cc299ec64a428ee6f9a35","observation_id":"4d6bf4b8-376c-446c-a051-7b3376dbd999","resolution":{"observed_at":"2026-08-11T19:01:27.457208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:26.879945Z","title":"Pvt v2: Improved baselines with pyramid vision transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.879945Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:b3ceb9151f212faefd17999ab9b42ed8a4235bc806d7659b927203282e13db48","observation_id":"f3151ea2-9bac-43ab-a0b0-d3ecc3ad7ae2","resolution":{"observed_at":"2026-08-11T19:01:26.879945Z","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":"2026-08-11T19:01:27.422325Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"8e02737b-ed98-429b-aae5-300e9d64ffd9","year":2016},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.885047Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:da1d4c82603f79704dd726a68982bbea8e5d382f8a776b4cc4caf25ad9cb5e43","observation_id":"cc6ea28f-8763-4279-98fd-c9ea64253200","resolution":{"observed_at":"2026-08-11T19:01:27.427972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.407068Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"436674a9-ef6c-4930-b4fa-3cc3ff35e1ac","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.889734Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:5b5e65d7ecc2f91499c9311255ca4a60305209950b0dcdd29a0987b077cc5b7b","observation_id":"2b5c4ff1-1abd-4e76-b853-500f355789d0","resolution":{"observed_at":"2026-08-11T19:01:27.412176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.390693Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":"957b25b5-0eab-4509-b857-5bad16597f88","year":2015},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.894163Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:0c3584de193521a117cd2d646aa12f7116b4ffcbb5b565723dda6a9ed79bc3b5","observation_id":"72837b38-c585-48b1-9a59-36d2a604b75f","resolution":{"observed_at":"2026-08-11T19:01:27.396532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.10127","last_updated":"2022-12-30T12:12:38Z","snapshot_observed_at":"2026-08-13T19:36:45.096291Z","submitted_at":"2021-04-20T17:12:51Z","title":"Generative Transformer for Accurate and Reliable Salient Object Detection","version":5},"cited_work":{"arxiv_id":"2104.10127","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.10127","snapshot_observed_at":"2026-08-11T19:01:27.068384Z","title":"Generative Transformer for Accurate and Reliable Salient Object Detection","venue":"cs.CV","work_id":"4fbd99a0-8271-49b3-897a-292c8d101299","year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.898572Z"},"links":{"cited_paper":"/paper/2104.10127","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:107445af813a897f503c227d17ddb2daf460c06ca02adfc67fcdf0aa2f76772d","observation_id":"82202fd0-3aa8-4320-a140-eb95f5efeb57","resolution":{"observed_at":"2026-08-11T19:01:27.075672Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.372884Z","title":"Encoder- decoder with atrous separable convolution for semantic image segmen- tation,","venue":null,"work_id":"5423ede2-969d-41e2-b904-8dbb373e25da","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.903440Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:6e42375b303cd681dce84b0d79a98022ed94c25b8b99d3cf17452a3981d27dfe","observation_id":"3c7f8533-a193-447f-8412-91d65c81e96f","resolution":{"observed_at":"2026-08-11T19:01:27.378338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-11T19:01:26.907749Z","title":"Mobilenets: Efficient convo- lutional neural networks for mobile vision applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.907749Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:34dd7a4742ba72eabae399adf1dc4162e03fe862bc66461b93f4438fdeed9bca","observation_id":"7e19158a-2e79-4438-8b22-651b33cc6ebd","resolution":{"observed_at":"2026-08-11T19:01:26.907749Z","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":"2026-08-11T19:01:27.356782Z","title":"Pyramid scene parsing network,","venue":null,"work_id":"af5b31fd-71b3-43a4-9dff-c3330c1efe6c","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.912473Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:953e81b66d00f7766b17b50d73d6917f154d9c8904ff6aa351cf88eb66a86cc6","observation_id":"fa73ccc1-9eea-4d94-af7f-4434ae1de5c0","resolution":{"observed_at":"2026-08-11T19:01:27.362058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.341095Z","title":"Fully convolutional networks for semantic segmentation,","venue":null,"work_id":"f9050743-ff28-4ef2-9cba-ed58f12cdcc2","year":2015},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.916741Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:70c2f367baa689fc1afcb5802d72fec22b0cab4e73af271861471fe4f981c993","observation_id":"dc56803e-c63d-4780-8ca4-564ea6a027fa","resolution":{"observed_at":"2026-08-11T19:01:27.346225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.325637Z","title":"Boosting adversarial attacks with momentum,","venue":null,"work_id":"168ba5f8-8072-4dc2-bea2-6344f6e8ac07","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.921128Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:cc5eb450fec9d7166b0bcdccb8e3908ac5d85f742d6a8430615a3080b8e2a74a","observation_id":"6d409f21-726e-45c3-9ae5-45bdac09558d","resolution":{"observed_at":"2026-08-11T19:01:27.330342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.309751Z","title":"Improving transferability of adversarial examples with input diversity,","venue":null,"work_id":"1a27f37a-fc89-42c8-b890-211a36e58ab0","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.926295Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:a5f1f9eb4cecc05fd26db7e60c7508926f1bc3858a4b04240234daa71e2a6c7a","observation_id":"86d86bce-c03c-470c-8400-3ae9004f0980","resolution":{"observed_at":"2026-08-11T19:01:27.315430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.293374Z","title":"Enhancing adversarial example transferability with an intermediate level attack,","venue":null,"work_id":"6b1e87ee-60ec-48c7-b661-395a86962d39","year":2019},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.930476Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:1c7bb039eb30b3425ebe36c8f46aec73493ae44da2a2f696d6e5b2e717299d39","observation_id":"1336eeea-86bb-4f89-9ee1-3e7d0510e873","resolution":{"observed_at":"2026-08-11T19:01:27.299552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.277838Z","title":"Improving adversarial transferability via neuron attribution-based attacks,","venue":null,"work_id":"364cc73e-1d90-46d0-a3db-18b82fcbfae9","year":2022},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.934696Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:420c90a6572722cb873efd0e8b2d925c9ea7eecb7149cf2e9cafb15705f32929","observation_id":"7dbfc57b-cb71-430c-ab5e-9e3d09de1ff8","resolution":{"observed_at":"2026-08-11T19:01:27.283161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.262675Z","title":"Enhanced-alignment measure for binary foreground map evaluation,","venue":null,"work_id":"0fbd38be-7dae-41b9-8085-d570b33de39b","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.938784Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:e58f9d6941534306d681d27edf73e84cd637631fca30e69e3e648530987a1d58","observation_id":"91662e94-c0e0-419b-834d-01ff50999721","resolution":{"observed_at":"2026-08-11T19:01:27.267365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.247251Z","title":"Structure-measure: A new way to evaluate foreground maps,","venue":null,"work_id":"20f60618-a25e-4604-9817-d036ee7781ae","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.942910Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:1ba1d97256238ec065c349bceea2ebc21f8fd6c0c25f8ab1687e224c5cf6b8fe","observation_id":"7cc15fa6-1433-4c3f-8b06-f144c911e604","resolution":{"observed_at":"2026-08-11T19:01:27.252226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11368","last_updated":"2021-11-22T17:26:21Z","snapshot_observed_at":"2026-08-13T18:16:35.436100Z","submitted_at":"2021-11-22T17:26:21Z","title":"Adversarial Examples on Segmentation Models Can be Easy to Transfer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.11368","snapshot_observed_at":"2026-08-11T19:01:26.947135Z","title":"Adversarial examples on segmen- tation models can be easy to transfer,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.947135Z"},"links":{"cited_paper":"/paper/2111.11368","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:79fd34b0b5561b06d8e54c75eef674c63221f6fb3250e3e22a0903d163fe0532","observation_id":"72a71ebe-0441-43b4-8008-ad8add7dc3a5","resolution":{"observed_at":"2026-08-11T19:01:26.947135Z","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":"2026-08-11T19:01:27.229866Z","title":"Prior convictions: Black-box adversarial attacks with bandits and priors,","venue":null,"work_id":"03b29200-ee43-4466-b78c-4989ae23b131","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.951748Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:aeacadd88db40aa70f87b0eae6a1132c85dff8817d0b22d65529b9e776551cec","observation_id":"7a0e08ad-1d7f-4c7c-8bab-e32820e69eba","resolution":{"observed_at":"2026-08-11T19:01:27.237092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07204","last_updated":"2020-04-26T22:20:25Z","snapshot_observed_at":"2026-07-06T05:43:27.961693Z","submitted_at":"2017-05-19T21:56:43Z","title":"Ensemble Adversarial Training: Attacks and Defenses","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07204","snapshot_observed_at":"2026-08-11T19:01:26.956059Z","title":"Ensemble adversarial training: Attacks and defenses,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.956059Z"},"links":{"cited_paper":"/paper/1705.07204","citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:ca017de60057f7c8750aa1221f0e2854d8948d3ff036a94a831b45a6018f6473","observation_id":"452cdd27-0bcf-44ca-b493-4cff1687058a","resolution":{"observed_at":"2026-08-11T19:01:26.956059Z","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":"2026-08-11T19:01:27.212829Z","title":"Learning to detect salient objects with image-level supervision,","venue":null,"work_id":"7b2a722d-db9b-40a7-932e-d9fba9e0e9d6","year":2017},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.960769Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:06367fea433b4721ddea2d49b9f7da582403aaf6ef0551e1e9e30909c8c4aa47","observation_id":"5188ed90-d0fa-42fe-853a-5b8a81c63a2e","resolution":{"observed_at":"2026-08-11T19:01:27.218139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.195238Z","title":"Design and perceptual validation of per- formance measures for salient object segmentation,","venue":null,"work_id":"600e1e8f-681d-429e-bf1d-b59ae255947a","year":2010},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.965238Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:ba76400675ce33c025d7279e11c70129c75819bcc610e37a3d7e48a0ae674368","observation_id":"a3b2c955-f6f7-4ee4-939b-0ce4ea602c2e","resolution":{"observed_at":"2026-08-11T19:01:27.200471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.178132Z","title":"Saliency detection via graph-based manifold ranking,","venue":null,"work_id":"4cee9a97-1e5f-4662-b727-9c49b5abb317","year":2013},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.969509Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:d8110a92d3464cb1baf7c933b2e918dba914603aeae745a982cddecc85c096b6","observation_id":"c1c0e4cc-2fd7-49fd-9a8c-18ab34918721","resolution":{"observed_at":"2026-08-11T19:01:27.183729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.161710Z","title":"Hierarchical saliency detection,","venue":null,"work_id":"64032cb3-88a7-4b43-a14e-614f67921f43","year":2013},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.974041Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:6cc5b5fd264c34ce8770ccde70b329b9cfd5b20bcd8ed71cb63bc084d716f339","observation_id":"3bd380fe-9fb2-4024-b431-db41b13d8243","resolution":{"observed_at":"2026-08-11T19:01:27.166796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.146512Z","title":"The secrets of salient object segmentation,","venue":null,"work_id":"ebbfcbe9-7400-4bcf-ab3a-20916f46e422","year":2014},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.978510Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:aca913888d94d3f4a1e86b270754342dde5a1bd01da584bc42a149c143da0a7c","observation_id":"947e175a-286d-495d-a1aa-9f8a6224ba6e","resolution":{"observed_at":"2026-08-11T19:01:27.151487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T19:01:27.131356Z","title":"Salient objects in clutter: Bringing salient object detection to the foreground,","venue":null,"work_id":"262427ca-8b18-46bf-8bc1-2e78f5c9a722","year":2018},"citing_paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T19:01:26.982853Z"},"links":{"citing_paper":"/paper/2412.07274"},"observation_digest":"sha256:04264d8b9c1a5dd257d543556fcbba647156be583dc7d41006472cf3f586fe50","observation_id":"c5047211-997f-4d0e-bf2a-eb0edca870f1","resolution":{"observed_at":"2026-08-11T19:01:27.136335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.07274","last_updated":"2024-12-10T08:02:27Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T18:53:42.615963Z","submitted_at":"2024-12-10T08:02:27Z","title":"A Generative Victim Model for Segmentation"},"reference_resolution":{"displayed":89,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":69},"total_outbound_references":89},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 0 inbound Pith citation observations for arXiv:2412.07274."}