{"as_of":"2026-08-14T18:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9ed28d193f17158438dec3075def7a48a1dbd358d4479d74a46f156eba4032ba","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:26:30.873480Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":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/2412.16651/citation-record","integrity":"/paper/2412.16651/integrity","json":"/paper/2412.16651/citation-record.json","paper":"/paper/2412.16651"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.531769Z","title":"Baseg: Boundary aware semantic segmenta- tion for autonomous driving,","venue":null,"work_id":"2c97732e-4d0f-4fd6-a4ac-e51fb78a499d","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.715004Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:5081e9daf3f442d0059949c4a4561e0465d3984ca2da5548a78e618786221fc1","observation_id":"3888836a-1fc5-4f3d-bcb5-49d8513e97c0","resolution":{"observed_at":"2026-08-11T10:26:31.536138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.517783Z","title":"Medical image segmentation using deep neural networks with pre-trained encoders,","venue":null,"work_id":"1d166f6d-0901-4a42-bacc-78d0a09c7d7c","year":2020},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.719856Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:f361d36ebb1fbbd4b3feed08c3fc513258a97a885cd411fbd9735c2977965eab","observation_id":"a0873b16-f2c0-4f8c-89e2-35b186d11dc2","resolution":{"observed_at":"2026-08-11T10:26:31.522352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.503621Z","title":"Incorporating deeplabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images,","venue":null,"work_id":"698d5096-3deb-4674-9098-5be25310da9d","year":2021},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.724481Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:6d8f0aba83e1392777930095599f546807e810b790103971e845690fe63d5398","observation_id":"d96460e9-8bc7-444e-b85f-ad74478c227c","resolution":{"observed_at":"2026-08-11T10:26:31.508235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.488657Z","title":"Pyramid scene parsing network,","venue":null,"work_id":"0cb4102b-0ee2-48c1-9613-788af54f6947","year":2017},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.728805Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:bcf7c3368c8d8ea6b7f03ccddcc7b480b7a7448c3d9aceb568f2ed4d49e54001","observation_id":"313faae9-6c60-4cdf-aed5-f4560dba3ee1","resolution":{"observed_at":"2026-08-11T10:26:31.493339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.474165Z","title":"Semantic image segmentation with deep convolutional nets and fully connected crfs,","venue":null,"work_id":"0e3ff481-694e-4c94-9d10-e36dd2cf512b","year":2015},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.733225Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:7fec61039292433d210b1247ed89762daf044f2c76eed0c778379c7bd8bb3974","observation_id":"db2e9831-9921-418b-bbc6-4fda746d45ed","resolution":{"observed_at":"2026-08-11T10:26:31.479031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.460853Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,","venue":null,"work_id":"9cd38165-7cbb-4d84-8650-75da097aff4d","year":2017},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.737980Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:dc65c2200a177ba1dc02068f63fe377f2dd021e58a99e2e215865e540d08bbad","observation_id":"4e173e81-a609-43c5-abe8-0deb4fcd7f71","resolution":{"observed_at":"2026-08-11T10:26:31.465337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.447907Z","title":"Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,","venue":null,"work_id":"33649f23-535c-4608-96f7-fc4b1212bde5","year":2022},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.743195Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:327d62fbd01849153f5aba86bc9977ce311878f8b25c6645909ada3342997a67","observation_id":"ff8f95a5-8139-4c1a-a8c7-88056ef6d2d5","resolution":{"observed_at":"2026-08-11T10:26:31.452262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02207","last_updated":"2023-12-03T00:48:33Z","snapshot_observed_at":"2026-08-13T05:16:27.914554Z","submitted_at":"2023-12-03T00:48:33Z","title":"TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.02207","snapshot_observed_at":"2026-08-11T10:26:30.747431Z","title":"Transegpgd: Improving transferability of adversarial examples on semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.747431Z"},"links":{"cited_paper":"/paper/2312.02207","citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:8d187d2712f37dd00236ff0d8bd883c8e9d9172813ce7e9efc933c1055862fdb","observation_id":"d65ea725-7de5-46d5-ac54-ca5c5bad89b2","resolution":{"observed_at":"2026-08-11T10:26:30.747431Z","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":"2026-08-11T10:26:31.434812Z","title":"Transferable adversarial facial images for privacy protection,","venue":null,"work_id":"9029bc77-eb68-4af0-b872-2bf8db1e462b","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.752554Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:11046123e20fd283ccbda15d437e206009540ea1840ce49e0a6d8e1c146e82a6","observation_id":"3a9abad4-e971-4a4b-9630-7ce1224eb919","resolution":{"observed_at":"2026-08-11T10:26:31.438729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.422129Z","title":"Numbod: A spatial-frequency fusion attack against object detectors,","venue":null,"work_id":"6d3834f3-7b5c-45be-a98e-6ed20f9921f5","year":2025},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.757037Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:5b78d6c5d89489e6b97c85260eeedd35192aebbadb2debc028adce6c80b1215c","observation_id":"3519a876-3a65-45b5-bcdb-ebea049cd757","resolution":{"observed_at":"2026-08-11T10:26:31.426190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.407482Z","title":"Adversarial machine learning in image classification: A survey toward the defender’s perspective,","venue":null,"work_id":"58aee68d-14be-4e1f-982f-7a92c706013e","year":2021},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.761278Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:e47d60e692f3de69e59d52b1c867d5eaa4ae1cb43aa932bc1aabf725f5c665f2","observation_id":"564a74e7-19c7-4afa-8aaf-82c0a4907dd5","resolution":{"observed_at":"2026-08-11T10:26:31.412182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.393204Z","title":"Downstream-agnostic adversarial examples,","venue":null,"work_id":"97764547-1c47-465c-b5d0-1c91996d9feb","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.765592Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:9cb91a1a3094d82a3b20a6eecc2cd17b8687cfe451d21f94e85295470ca1f28f","observation_id":"ab17ed68-ee2c-4baa-878b-2602fec3f807","resolution":{"observed_at":"2026-08-11T10:26:31.397939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.378845Z","title":"Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning,","venue":null,"work_id":"5756e6c9-25ce-4410-9418-b21f6ec5f846","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.770314Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:fc166636fdee55b5ff50a84e69bfcd90f8ace53f525f72e9ed00932e7cf9afa6","observation_id":"b2ae835e-45a5-4c59-95da-68f7e3c29c27","resolution":{"observed_at":"2026-08-11T10:26:31.383700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.364756Z","title":"Darksam: Fooling segment anything model to segment nothing,","venue":null,"work_id":"5ab53ddc-05f1-48ee-ac89-958810ecf495","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.774880Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:54462744d7130f87be62489e380c1b18e18ed605dc69ecac8efeb847d1b82d3c","observation_id":"49ed2eb4-affe-478c-90f3-0ca92cca2014","resolution":{"observed_at":"2026-08-11T10:26:31.369132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.350758Z","title":"Universal adversarial perturbations against semantic image segmentation,","venue":null,"work_id":"312050a1-165f-461b-9f14-ca245d896190","year":2017},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.779318Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:3fe6fdbc43f1e18591744eaf3559a1ef89b1c41fc3cb307a96a0ca834fcb47a0","observation_id":"15914f7b-69b3-45b4-a16f-788a43ce6cd1","resolution":{"observed_at":"2026-08-11T10:26:31.355413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.336722Z","title":"Improving transferability of generated universal adversarial perturbations for image classification and segmentation,","venue":null,"work_id":"14207e8b-5179-4cd3-88d9-11c0e2ea9a83","year":2022},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.783719Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:8c53f5b7bca8f0fa264d6f8f2e63b6d2ffd3e2c37c14738bcdefb9d27d677703","observation_id":"7f5f0766-8a5d-43bf-8c7d-3e712d3d2dd9","resolution":{"observed_at":"2026-08-11T10:26:31.341489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.324034Z","title":"Universal adversarial perturbations,","venue":null,"work_id":"78c3993f-b8f9-426e-826c-075bb9b6fa05","year":2017},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.787890Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:53b57115567732c82d582f9dfd6964820fb75074aa0564b1cac74dbd9d53e809","observation_id":"d2077bd6-0d6c-4c7a-99fa-7c02597b4353","resolution":{"observed_at":"2026-08-11T10:26:31.328143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.310702Z","title":"Universal adversarial attack via enhanced projected gradient descent,","venue":null,"work_id":"727e421e-6127-4023-8f10-dcb91a68cc97","year":2020},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.792216Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:774b543df6e801850cb3a67749df8cfc331762e789d794f3de4a5f24490102f3","observation_id":"6e00b12e-51ab-476e-ba32-50251d873695","resolution":{"observed_at":"2026-08-11T10:26:31.315189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.297277Z","title":"Prototype and context-enhanced learning for unsupervised domain adaptation semantic segmentation of remote sensing images,","venue":null,"work_id":"3a16ea78-b9d8-4dee-9202-c9c6594c246d","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.796649Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:8e6f11bf83bc395882ca9acb8a0d2c2ee9bc8e1e1319e46c3c04818ec33f5e45","observation_id":"07877c87-44cd-431f-9db6-4e499e9425d3","resolution":{"observed_at":"2026-08-11T10:26:31.301554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.282766Z","title":"Fpanet: Feature pyramid aggregation network for real-time semantic segmenta- tion,","venue":null,"work_id":"f5d8eb3f-c488-454d-9e4e-c0438f39e81e","year":2022},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.801090Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:27ebba7fe74a053f546d23b616115bdc0b8b6b82914d7e64cd33cb007f2d977d","observation_id":"cfe01e04-9b11-42bb-a8f9-08417a308ca9","resolution":{"observed_at":"2026-08-11T10:26:31.288258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.267217Z","title":"An encoder-decoder network based fcn architecture for semantic segmentation,","venue":null,"work_id":"01ee60aa-bc71-4621-b7c7-87367ca8095f","year":2020},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.805522Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:e951aa7a06d1426baaf7cff08ed068a72ba782de6dbc2218068604ce45b63167","observation_id":"652a8765-4137-4b03-9ab1-e3d43ab8629f","resolution":{"observed_at":"2026-08-11T10:26:31.272799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.252000Z","title":"Cgnet: A light-weight context guided network for semantic segmenta- tion,","venue":null,"work_id":"15122080-3035-40dd-9650-c1a278438985","year":2020},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.809674Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:9f59e6b25183f2c4e8c2fd87779eba0d1680e8978bb59f697dd382a8e7e01a88","observation_id":"30b26ec2-28f7-4f49-991a-55bf7bfba86c","resolution":{"observed_at":"2026-08-11T10:26:31.257139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.237190Z","title":"Forest segmentation with spatial pyramid pooling modules: a surveillance system based on satellite images,","venue":null,"work_id":"4b149fdf-a292-4191-a6b0-6fa9b645cf88","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.814022Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:f5ff66d65bc1e5409fd65ce3931d91cb3c8cbced37b88afac1f3f5f32c856fe8","observation_id":"db8913c9-ed9e-4d1b-9a93-4fe4709d5df1","resolution":{"observed_at":"2026-08-11T10:26:31.242536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-11T10:26:30.818258Z","title":"Rethinking atrous convolution for semantic image segmenta- tion,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.818258Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:e77846369ac8e055b8f52fbc2793fe497f023ce3d82e1b109c7c4426053f3707","observation_id":"f3b69408-82a6-4a22-972e-7be87d0e0020","resolution":{"observed_at":"2026-08-11T10:26:30.818258Z","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":"2026-08-11T10:26:31.221798Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation,","venue":null,"work_id":"a45c39f0-a5c5-4891-9cb2-fa06dadfb083","year":2018},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.822901Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:d8ec2f79332e4e2abd0c594c36b14638eda85d86834a4350167cdaa955e107b3","observation_id":"bcce9198-7144-4506-8abf-d88d72db35c6","resolution":{"observed_at":"2026-08-11T10:26:31.227464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.206636Z","title":"Denial-of-service or fine-grained control: Towards flexible model poisoning attacks on federated learning,","venue":null,"work_id":"ca371b7f-0e67-4639-b0f9-d94482ec2729","year":2023},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.826714Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:373503da3da783ed619792ce8aa6de6000e350d736c92b8d626fd9d7dcfda276","observation_id":"31504737-6b8a-4b7d-ad7e-8bd88dcdac8b","resolution":{"observed_at":"2026-08-11T10:26:31.211989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.191340Z","title":"Unlearnable 3d point clouds: Class-wise transformation is all you need,","venue":null,"work_id":"e92c98ba-ec52-478a-918b-e4eecfdff5e2","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.830669Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:1177d2bfb4f85722c10e91609d13eecb2a2fe294340a9909e60f8752bca0e7b6","observation_id":"c5637817-9650-4c53-9d14-a41112015d74","resolution":{"observed_at":"2026-08-11T10:26:31.196687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.175473Z","title":"Badhash: Invisible backdoor attacks against deep hashing with clean label,","venue":null,"work_id":"08e2f1df-4187-4a7a-97c8-6e7a0f7e2b15","year":2022},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.834561Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:1066510ed883ffeacbc25aba5e9758fac99e850fb2a97028c463248e29a84adb","observation_id":"0f050c6e-6f31-4815-b570-7730f8b53f8f","resolution":{"observed_at":"2026-08-11T10:26:31.180805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.159953Z","title":"Detector collapse: Backdooring object detection to catastrophic overload or blindness,","venue":null,"work_id":"ef699c79-06a0-4ab0-a451-7522483a5954","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.838967Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:7186bc69e7245239e41f6bc5fdbedfc8d57762fc4b0d2a3df495d6928b44e3e3","observation_id":"cc034115-fa3a-4144-96a1-caf0aa80a450","resolution":{"observed_at":"2026-08-11T10:26:31.165275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:30.842775Z","title":"Trojanrobot: Backdoor attacks against robotic manipulation in the physical world,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.842775Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:d108ca73f1b5d059a6f359706306d5365b1c11b6e420f1e3ee5bddd05206008b","observation_id":"e8d47471-a0dc-493e-aeb3-8af88cb5ac0c","resolution":{"observed_at":"2026-08-11T10:26:30.842775Z","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":"2026-08-11T10:26:31.144703Z","title":"Reverse backdoor distillation: Towards online backdoor attack detection for deep neural network models,","venue":null,"work_id":"1ade64de-93a6-4d49-96ef-8070e5877df8","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.846560Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:695283d33d82e338593e4f0a1cc91fe0be103c6390f73c366039d53eefa1051f","observation_id":"cab9cb5a-94d6-4f1f-bb59-2f87e31fd71e","resolution":{"observed_at":"2026-08-11T10:26:31.150082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.20242","last_updated":"2026-06-09T05:19:18Z","snapshot_observed_at":"2026-08-13T04:16:56.340592Z","submitted_at":"2024-07-16T13:13:16Z","title":"BadRobot: Jailbreaking Embodied LLM Agents in the Physical World","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.20242","snapshot_observed_at":"2026-08-11T10:26:30.850367Z","title":"Badrobot: Manipulating embodied llms in the physical world,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.850367Z"},"links":{"cited_paper":"/paper/2407.20242","citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:d53b97aa1823a578ac18e495eed0ec239145f1ab10f09f0856c5dbe0b2e56002","observation_id":"83301a7a-1f6d-4b8b-8feb-2e45a9cc4559","resolution":{"observed_at":"2026-08-11T10:26:30.850367Z","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":"2026-08-11T10:26:31.128496Z","title":"Breaking barriers in physical-world adversarial examples: Improving robustness and transferability via robust feature,","venue":null,"work_id":"cc3565c8-c42d-4d7a-908d-a3f435bec048","year":2025},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.854563Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:949bef9dbf83979477d08174c81600b974caea22ff125dbe6d1dd6fd6bb25ec5","observation_id":"3160237d-6436-4efc-9f20-5caa834757f0","resolution":{"observed_at":"2026-08-11T10:26:31.133717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.113182Z","title":"Data-free universal adversarial perturbation and black-box attack,","venue":null,"work_id":"f73fe0c0-ca5b-4e24-a498-5299135b669e","year":2021},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.858338Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:ecb749fe3a8fd257d5366b62bb19c5380f1e7d7006acb00b7c54f0ed7b4fdec9","observation_id":"6fae2a93-0602-49e7-bd8e-85404ac33029","resolution":{"observed_at":"2026-08-11T10:26:31.117737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.098628Z","title":"Frequency-driven imperceptible adversarial attack on semantic similarity,","venue":null,"work_id":"39abc8ba-30b7-4f5d-972f-6239a8a91a2a","year":2022},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.862036Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:efdec73198c0615c4fc93bea7f15d3146d98c2b5e46f6826dfab0a1c7fe7e014","observation_id":"35309963-2c6e-489e-ad95-a07c565d5bc6","resolution":{"observed_at":"2026-08-11T10:26:31.103397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.084665Z","title":"The pascal visual object classes (voc) challenge,","venue":null,"work_id":"2f56fad9-2ee4-42b0-a71e-d8f66dee2306","year":2010},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.865702Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:781876eb1f7795bff362f323b54243eec230999071713b9a15b5617942463660","observation_id":"eddad27c-3026-4d2c-a912-e08c205fd98b","resolution":{"observed_at":"2026-08-11T10:26:31.088964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.071244Z","title":"The cityscapes dataset for semantic urban scene understanding,","venue":null,"work_id":"50d72ea9-d7b1-4e66-89e3-0d738b1b1223","year":2016},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.869692Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:5443bdbf39dcd0d43ec8eed39c088fc4f8e030bb432e6415e5a726783cf634f6","observation_id":"cf32e2f2-0df3-4f5d-9379-99b4dc37e0bb","resolution":{"observed_at":"2026-08-11T10:26:31.075347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:26:31.054154Z","title":"Securely fine-tuning pre-trained encoders against adversarial examples,","venue":null,"work_id":"4940b69c-0571-4b0d-8941-3c6df649c2e8","year":2024},"citing_paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T10:26:30.873480Z"},"links":{"citing_paper":"/paper/2412.16651"},"observation_digest":"sha256:ba4efdee3ca537f4e6b2c471c5dfe80a5ac843f3000f6e648c7a4ee1b2d8409f","observation_id":"5ba4a65e-5ce8-4775-a78f-826b442ae8fb","resolution":{"observed_at":"2026-08-11T10:26:31.061211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16651","last_updated":"2025-01-03T15:39:46Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T10:20:44.123336Z","submitted_at":"2024-12-21T14:46:01Z","title":"PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":34},"total_outbound_references":38},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.16651."}