{"as_of":"2026-08-12T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b582407e3eec1c171fa8c1291cdad66d392e8e72c75398455a8187580df526f1","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:07:33.024715Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:26:58.647254Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T15:27:01.283805Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"cited_work":{"arxiv_id":"2411.18572","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.18572","snapshot_observed_at":"2026-08-07T15:27:01.283805Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","venue":"cs.CV","work_id":"cb26d70e-9c21-445a-8120-e2955706b4fa","year":2024},"citing_paper":{"arxiv_id":"2505.15233","last_updated":"2025-05-21T08:11:07Z","snapshot_observed_at":"2026-08-08T15:32:36.181532Z","submitted_at":"2025-05-21T08:11:07Z","title":"CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T15:26:58.647254Z"},"links":{"cited_paper":"/paper/2411.18572","citing_paper":"/paper/2505.15233"},"observation_digest":"sha256:083f0e9aef11f91355ba668ff876debd3ba9be76e6ea59d4d15ad6373f7be58a","observation_id":"97f193b6-0890-403e-8b58-e6db7fecf709","resolution":{"observed_at":"2026-08-07T15:27:01.335494Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.18572/citation-record","integrity":"/paper/2411.18572/integrity","json":"/paper/2411.18572/citation-record.json","paper":"/paper/2411.18572"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.680189Z","title":"Deferred neural rendering: Image synthesis using neural textures,","venue":null,"work_id":"26465743-3ed3-41ce-941c-9bbdd6cb8075","year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.804547Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:d80b566db8b53d956d3603b5a7fce0fc01a31f7a50fdde8bfb147fa5334c94c2","observation_id":"a0c12662-291a-4d49-b290-38649dc5233a","resolution":{"observed_at":"2026-08-12T11:07:33.684107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.808990Z","title":"Face2face: Real-time face capture and reenactment of rgb videos,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.808990Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:fbdf80d4d76e513272360efbf9beffa2594f30714b87c2eeeba44a7e52af44e8","observation_id":"5180e1e5-39f5-4190-8aca-2e25ffe74e70","resolution":{"observed_at":"2026-08-12T11:07:32.808990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.05535","last_updated":"2021-06-29T07:07:57Z","snapshot_observed_at":"2026-08-12T08:51:14.686364Z","submitted_at":"2020-05-12T03:26:55Z","title":"DeepFaceLab: Integrated, flexible and extensible face-swapping framework","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.05535","snapshot_observed_at":"2026-08-12T11:07:32.812700Z","title":"Deepfacelab: Integrated, flexible and extensible face-swapping framework,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.812700Z"},"links":{"cited_paper":"/paper/2005.05535","citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:5a3f9079edbd16f34f2e408535152249c92940757849a1a460ed3e7837bad288","observation_id":"55651918-2d42-41e5-aafb-28bc7622c3ed","resolution":{"observed_at":"2026-08-12T11:07:32.812700Z","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":"2026-08-12T11:07:33.662801Z","title":"Poisson image editing,","venue":null,"work_id":"d0443eee-6201-4975-877e-a814d339eec2","year":2003},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.817019Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:7009330b992d4aeb2a6d6cde0ac49a1a0f39ccb410eb9be02fbcc7f7ab269524","observation_id":"79d2bcb0-d607-4f51-8df3-b1491ab98a6b","resolution":{"observed_at":"2026-08-12T11:07:33.666434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.13457","last_updated":"2020-09-15T07:43:58Z","snapshot_observed_at":"2026-08-08T08:09:14.122603Z","submitted_at":"2019-12-31T17:57:46Z","title":"FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.13457","snapshot_observed_at":"2026-08-12T11:07:32.820802Z","title":"Faceshifter: To- wards high fidelity and occlusion aware face swapping,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.820802Z"},"links":{"cited_paper":"/paper/1912.13457","citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:72a4b6e56aadc5a0c92ef6ad11b30b0300af1a5a3644a42e92bfeb9daa25651a","observation_id":"a4f60b6b-8ee5-47d0-90ef-85c7ffdeb890","resolution":{"observed_at":"2026-08-12T11:07:32.820802Z","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":"2026-08-12T11:07:33.652150Z","title":"Fakepolisher: Making deepfakes more detection- evasive by shallow reconstruction,","venue":null,"work_id":"a84fa3a6-d7a5-41c2-b0dd-592f5b4dc47c","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.824955Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:4eb8e2daae2e155f8e5e44215ab6a01e4158c4efc298ad4b4714d02ff9968154","observation_id":"8eb890e3-9a89-4a3e-9c04-a303e0900ff9","resolution":{"observed_at":"2026-08-12T11:07:33.655805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.641375Z","title":"Faceswap,","venue":null,"work_id":"e59240a6-0314-4b78-b57b-566bc80c629c","year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.829766Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:511b224f388f18850d74e08eb1b1e561500d4a9158def45471ef38b5f7ba43ad","observation_id":"69ff6a46-df26-4d1b-913e-0ad8edc032ef","resolution":{"observed_at":"2026-08-12T11:07:33.644894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.833340Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.833340Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:f819c932578530461c3db270f83ae2a017a650b34d0682d1b6f3de0aafcc1a0a","observation_id":"8c50a5c2-1796-41bf-9d17-416aa6c00a61","resolution":{"observed_at":"2026-08-12T11:07:32.833340Z","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":"2026-08-12T11:07:33.624327Z","title":"Faceforensics++: Learning to detect manipulated facial images,","venue":null,"work_id":"e59ae571-0acb-4f5a-85f2-87c29d5acac0","year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.836913Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:a08ed22d8d9b93ddd4b13de825de8f7aed4f98951a062852d85be48227186690","observation_id":"c350d576-320b-41d0-8865-ce76d3c472bf","resolution":{"observed_at":"2026-08-12T11:07:33.628004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.840397Z","title":"Mesonet: a compact facial video forgery detection network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.840397Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:21a1dd723d3da00c28c4bc7a08694300723dcb14923d43d6d5c64ef107435d43","observation_id":"8646e044-3b3f-46c4-a7aa-7293321f1142","resolution":{"observed_at":"2026-08-12T11:07:32.840397Z","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":"2026-08-12T11:07:32.843967Z","title":"Efficientnet: Rethinking model scaling for con- volutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.843967Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:b35caf1a482abfa368348b8ecad26ca5cc33ece047f3c60e94889812365a3a03","observation_id":"31911a82-d03a-44a8-b6ff-77d75931f852","resolution":{"observed_at":"2026-08-12T11:07:32.843967Z","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":"2026-08-12T11:07:32.847676Z","title":"Face x-ray for more general face forgery detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.847676Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:601650ab17ec31ccd24ca02e980c54e9a2abe61391c3b95eb103eeb808287aec","observation_id":"4e7dacbe-72e6-4e14-a1e5-571978893f41","resolution":{"observed_at":"2026-08-12T11:07:32.847676Z","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":"2026-08-12T11:07:33.593764Z","title":"Exposing deepfake face forgeries with guided residuals,","venue":null,"work_id":"d271f0b8-d72b-4476-a6dd-d91736bff58d","year":2023},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.851157Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:bb687652eb0048d4e38ab153bbef7dc98b47a9bf74db2022fce51d34266e020f","observation_id":"6a820a7a-1dd6-4928-9d3d-8e2babb6f2aa","resolution":{"observed_at":"2026-08-12T11:07:33.597585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.583204Z","title":"Id- reveal: Identity-aware deepfake video detection,","venue":null,"work_id":"381992da-a55b-49a0-a612-7b8f4aca1550","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.854602Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:513da878b13ff8bb33a69006838fa85089dc5112fcde1e373ff95b1faa7623f0","observation_id":"c0217c8f-b46a-402c-92f0-77312f4d4620","resolution":{"observed_at":"2026-08-12T11:07:33.587060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.572391Z","title":"Wilddeepfake: A challenging real-world dataset for deepfake detection,","venue":null,"work_id":"d6b55770-315a-412e-9378-c86fd880455d","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.858702Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:851a47b9e56c9d228111ede2db1282b1f4391005211758bba463ae6c5ab12103","observation_id":"fcf4e7da-57f6-4067-884e-8d61cae25a37","resolution":{"observed_at":"2026-08-12T11:07:33.575879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.561955Z","title":"Finding facial forgery artifacts with parts-based detectors,","venue":null,"work_id":"3b13107a-5ddd-47d8-8985-2787eda8caa6","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.862484Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:a686e8904f91a755dca1aac53a63b33273125c122290fca65460e7a42eb733a3","observation_id":"87120220-90dd-42f7-a5c9-dc7bad93a519","resolution":{"observed_at":"2026-08-12T11:07:33.565532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.551411Z","title":"Exploiting fine- grained face forgery clues via progressive enhancement learning,","venue":null,"work_id":"e9d8d279-8395-4ff0-967a-ac954983e29f","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.866263Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:02f836597e70b08d4b7b9353d4b5ef9946343a117bbe20c27af82a1c6382255f","observation_id":"a6d3ec91-a1b2-47e5-95d2-603a48e7af21","resolution":{"observed_at":"2026-08-12T11:07:33.555028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.540697Z","title":"Local relation learn- ing for face forgery detection,","venue":null,"work_id":"32dc216e-1218-4841-87f8-d5798d24e217","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.870060Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:de06c81b73af1e9e0e7f05c12e8fdb5094e4d2f45a8ead0652193f3d3bc646ad","observation_id":"6d0506e4-d058-4089-8634-359238c7738c","resolution":{"observed_at":"2026-08-12T11:07:33.544896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.530385Z","title":"Face forgery detection by 3d decomposition,","venue":null,"work_id":"e9e2782a-6046-4a9e-afe1-31577565a49c","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.873765Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:07634d0828c9fb07460536abbdb84edbd51a880cc09b69ec7e765bab5b7e7f42","observation_id":"2656e705-6dd6-4a8e-9bcd-62e97f1c3529","resolution":{"observed_at":"2026-08-12T11:07:33.533942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.877396Z","title":"Multi- attentional deepfake detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.877396Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:e201d0b5ec95ebebd7cb6c782eeaade83ac4b1433122ac99c809ceb304b8b273","observation_id":"e3a11205-b07f-4985-a1b7-37a15a6290ce","resolution":{"observed_at":"2026-08-12T11:07:32.877396Z","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":"2026-08-12T11:07:33.512560Z","title":"Pixel bleach network for detecting face forgery under compression,","venue":null,"work_id":"75a5ab16-c6ad-4fdd-bb96-e7c2fa179787","year":2023},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.880965Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:eabdcd24fc36db5ac8b2086d685db3519dbb533ce67cd73ddfd6b04339e4ef66","observation_id":"88ed097a-3034-4fd3-badc-99aecc1c3d34","resolution":{"observed_at":"2026-08-12T11:07:33.516147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.501545Z","title":"Exposingaicreated fakevideosbyde- tectingeyeblinking,","venue":null,"work_id":"f1f62b19-6c3a-4e75-b705-c41a82b3a3be","year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.884879Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:3e397e1545211d1e3e817abcf09ddb90bae8187b058b51c9fcb4a4f08f8e4273","observation_id":"04305215-14b4-48a1-85d3-0eef1300604b","resolution":{"observed_at":"2026-08-12T11:07:33.505394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.490276Z","title":"Lips don’t lie: A generalisable and robust approach to face forgery detection,","venue":null,"work_id":"ba8a7bcb-beec-43b2-b717-aeca4c56aca1","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.888459Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:58ec19c0b4548cce0220233998ff37fbcbfcfa99c13b172efa6b4f3dff8f6f6f","observation_id":"8f9fd1d2-c27d-4029-8522-16dae37f0933","resolution":{"observed_at":"2026-08-12T11:07:33.494001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.479419Z","title":"Poster: Towards robust open-world detection of deepfakes,","venue":null,"work_id":"4cbbf819-f62f-4b47-a9fd-1e65f329578f","year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.891953Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:f8ea7bdba8612a55f0dc3a96381a77e5d565fe1fd64b33d330f5997ade99b422","observation_id":"43111c0c-915d-4826-af31-8ccdfd2eafce","resolution":{"observed_at":"2026-08-12T11:07:33.483183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.468868Z","title":"Capsule-forensics: Using capsule networks to detect forged images and videos,","venue":null,"work_id":"f24cd495-1665-440a-9d96-4837011082bb","year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.895479Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:1647c7c28ca59684b26385789cb88c0f35e6c66e0f90d295dd35afca01a9ac7d","observation_id":"78747b1b-67d9-4595-98e5-09980a4585aa","resolution":{"observed_at":"2026-08-12T11:07:33.472486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.457799Z","title":"Exploring temporal coherence for more general video face forgery detection,","venue":null,"work_id":"d5803f3b-9d3d-4212-941d-81bd7cc75aad","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.898890Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:0455dde2aa3faacac9954b41867a6f3b9e4af9be79c24ccfe1f155da85800972","observation_id":"34645832-9ac8-4565-ab90-6cc59c42f9ee","resolution":{"observed_at":"2026-08-12T11:07:33.461773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.447424Z","title":"Spatiotemporal inconsistency learning for deepfake video detection,","venue":null,"work_id":"9fc6d5a5-e114-42c9-aaae-6d26f392a272","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.902259Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:e226ee213ef9b84ffe33159e8425c0b7b4aac0c336376d5772901630e826e3e1","observation_id":"4494c3f5-c84d-4e5e-8d0a-c0f3491676ab","resolution":{"observed_at":"2026-08-12T11:07:33.451101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.905870Z","title":"Detecting deepfake videos with temporal dropout 3dcnn","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.905870Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:72a9dc15775b47ef26c737fa769e1e33c3232aa03c1ccb3aa4addbb1a3a919bb","observation_id":"ce9e20e1-51cb-43b8-bced-aaa13d671022","resolution":{"observed_at":"2026-08-12T11:07:32.905870Z","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":"2026-08-12T11:07:32.909510Z","title":"Deepfake video detection with spatiotemporal dropout transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.909510Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:316524289d3c51a5abfef0f2442c6136d25864fe3419a0f169dd644e1dc2a18d","observation_id":"fdfa1872-22ed-4666-be8e-903469feb17a","resolution":{"observed_at":"2026-08-12T11:07:32.909510Z","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":"2026-08-12T11:07:33.430638Z","title":"Delving into the local: Dynamic inconsistency learning for deepfake video detection,","venue":null,"work_id":"116fc1be-007d-49d3-a7fd-11f43eb3cfba","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.913084Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:584bd0199d7b487930fcb2004fbc666007a2e0fac62713ef66b68b0996bcb502","observation_id":"f28d1b90-d635-4d98-89d6-0c0b174dbd3c","resolution":{"observed_at":"2026-08-12T11:07:33.434532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.420245Z","title":"Unsupervised learning-based framework for deepfake video detection,","venue":null,"work_id":"35ba1b19-04b5-414f-aad0-c3f768e36faa","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.916484Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:315a548c21ff1188e0f2ea341f54810cc126d928be4c8cb3346944520f25068a","observation_id":"bb4d9c50-2e19-4054-a2ce-7e5747cfffb9","resolution":{"observed_at":"2026-08-12T11:07:33.423842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.919883Z","title":"Celeb-df: A large- scale challenging dataset for deepfake forensics,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.919883Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:633e50f5c1ccc5f2902ed10935e9305a6dbd72ee174e1bc43abeba23b59d0a21","observation_id":"0bd97d3d-e946-479f-821e-41154c45b555","resolution":{"observed_at":"2026-08-12T11:07:32.919883Z","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":"2026-08-12T11:07:33.403117Z","title":"Joint 3d face recon- struction and dense alignment with position map regression network,","venue":null,"work_id":"a3f1c3db-cc1b-4790-92da-11646574426e","year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.923375Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:297c5bda70029c91f345e3d5d193ce768523eb38d7a99b5367d325f1292c00a8","observation_id":"6f90dd4a-df1d-442b-90c3-0529369d2275","resolution":{"observed_at":"2026-08-12T11:07:33.406859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.392622Z","title":"Face depth prediction by the scene depth,","venue":null,"work_id":"975b92d7-5460-41e0-b7dd-71954f8dda3d","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.926890Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:3b7c0a9a8ac79d6bf8a27d60d5b67967d9618a2898e0d4740e67374234a55e3f","observation_id":"467396a5-bae5-4331-a18a-c66f2f9fb6c3","resolution":{"observed_at":"2026-08-12T11:07:33.396385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.381317Z","title":"Dual spoof disentanglement generation for face anti-spoofing with depth uncertainty learning,","venue":null,"work_id":"925f9f2b-3e75-4d88-aebf-45d764ebd103","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.930334Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:fc96e3fe9336f6dee80b81936d54c37ec583738bdf251887af5a55d3e330501e","observation_id":"a94098e7-260d-40cf-9b9b-bb671baa7a08","resolution":{"observed_at":"2026-08-12T11:07:33.385393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.370769Z","title":"Facial depth and normal estimation using single dual-pixel camera,","venue":null,"work_id":"0db760db-d135-4e56-8eff-2a919b5ff199","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.933924Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:1afd9c210332abf799b28c281a7b40dc21a3743d7e6a5b2b4bd6745b55fc4cc4","observation_id":"71242bae-6679-4635-89e6-cae673601960","resolution":{"observed_at":"2026-08-12T11:07:33.374582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.937320Z","title":"Xception: Deep learning with depthwise separable convolu- tions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.937320Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:0f2c5a7f49ac5c99ae2b9e6256c5a5b9e00ce54debab509a9bbd689f635494ef","observation_id":"921ae94b-699a-4db0-ba11-5f6277ff114a","resolution":{"observed_at":"2026-08-12T11:07:32.937320Z","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":"2026-08-12T11:07:33.354091Z","title":"Stargan: Unified generative adversarial networks for multi-domain image-to- image translation,","venue":null,"work_id":"f404d33f-50f3-4cec-8af2-615ba19b90b1","year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.940666Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:bbb8041cb8fc838bd5f0e54a8632f212e33094a8676a83cd63c293533ae47a6d","observation_id":"00e1454f-dcda-4ce3-abec-6e31846b478b","resolution":{"observed_at":"2026-08-12T11:07:33.357776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.944559Z","title":"Attgan: Facial attribute editing by only changing what you want,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.944559Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:551a294b8d2c1b72ae91c7b62a9dd41459e1a3b025b67f1debf139e86a53b596","observation_id":"d9ab6ff4-cb35-4f6b-85d8-f2f647b0711a","resolution":{"observed_at":"2026-08-12T11:07:32.944559Z","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":"2026-08-12T11:07:32.947901Z","title":"Cnn- generated images are surprisingly easy to spot... for now,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.947901Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:5cd16448fc31cd2050b42b49c64242e4d93ec2bbf8740758dc04f99b46e9dbdf","observation_id":"8e93fef8-2b0d-440f-a097-ccf9319c76d5","resolution":{"observed_at":"2026-08-12T11:07:32.947901Z","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":"2026-08-12T11:07:33.332990Z","title":"What makes fake images detectable? understanding properties that generalize,","venue":null,"work_id":"4e929f3f-c87e-4595-93c4-dd66ae73821e","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.951067Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:583b3eff22e6c9064ffb35064e5406fa6ffacf268ff73b69dd0ecbe5d03833e8","observation_id":"3a4e077f-cc8c-41f4-b421-42563761c53f","resolution":{"observed_at":"2026-08-12T11:07:33.336311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.954287Z","title":"Learning self- consistency for deepfake detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.954287Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:99e4ff209d031c6b129ceda7b8c8451e3ac5a00c82320070b2a4e37e0858acc1","observation_id":"1ab397c3-fc98-4f46-b063-a9e270375cf7","resolution":{"observed_at":"2026-08-12T11:07:32.954287Z","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":"2026-08-12T11:07:33.317281Z","title":"Patch diffusion: a general module for face manipulation detection,","venue":null,"work_id":"449be144-1304-4761-914c-e83abbd6f042","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.957887Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:30eb427a3557115b76792bf4d5b013b5e76e561b9ea709408693ee872465b90c","observation_id":"5c2dd611-0066-45fb-8382-1652f4e251f3","resolution":{"observed_at":"2026-08-12T11:07:33.320999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.961541Z","title":"On the detection of digital face manipulation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.961541Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:d3741a95b4d8d19392620720ebd608f0ba2472b7c9b7faf67520f159896af95d","observation_id":"10d1b1ce-5af9-450d-b0da-8c77a013a29b","resolution":{"observed_at":"2026-08-12T11:07:32.961541Z","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":"2026-08-12T11:07:33.301287Z","title":"Two-branch recurrent network for isolating deepfakes in videos,","venue":null,"work_id":"a61eb293-8405-4b28-a78d-856c3883eb55","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.965229Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:07f71bfab87e7c34aedafed5eadcacc1f69e66eede8d5d962a6430ccf191c826","observation_id":"0b616ba4-6fae-458a-823e-20ef3c9103d8","resolution":{"observed_at":"2026-08-12T11:07:33.304845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.968523Z","title":"Leveraging frequency analysis for deep fake image recogni- tion,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.968523Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:77098767167ed7692179435d47d1c2038b8da1169924ec81579f3a1f4ed055c5","observation_id":"392f6f2f-5073-4f82-b892-147b466b6542","resolution":{"observed_at":"2026-08-12T11:07:32.968523Z","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":"2026-08-12T11:07:33.284557Z","title":"Thinking in frequency: Face forgery detection by mining frequency-aware clues,","venue":null,"work_id":"8882e13d-f9e6-4ecc-99a0-7ac7032b4249","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.971959Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:3f2fc0e49620bb9ac3fd6b314d298a75c02b2bd0a0c2414e1755d79ddebb712d","observation_id":"58a0c89e-f4b3-417e-9a8e-fb7a957bf965","resolution":{"observed_at":"2026-08-12T11:07:33.288168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.273690Z","title":"High-accuracy rgb-d face recognition via segmentation-aware face depth estimation and mask-guided attention network,","venue":null,"work_id":"f4212902-e419-4530-9a2e-d85db88be6b0","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.975548Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:5162aec3dba5310a0d19bb47222c580c1ec77639915056cf3dacf19c2637deae","observation_id":"7560f9c9-0de6-49c3-8e98-0772918d61b1","resolution":{"observed_at":"2026-08-12T11:07:33.277761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.263279Z","title":"Deep spatial gradient and temporal depth learning for face anti- spoofing,","venue":null,"work_id":"88b39eec-98f4-4a44-bb6f-9999be4c6244","year":2020},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.979056Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:40f527a7b3e6bb8e82d680f0388dc02640ab8d12e595c650d069d970fe84934d","observation_id":"228657b1-336c-4822-9c9d-c78a8c08b8fd","resolution":{"observed_at":"2026-08-12T11:07:33.267066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.252792Z","title":"Attention-based spatial- temporal multi-scale network for face anti-spoofing,","venue":null,"work_id":"e66cf508-8e36-4739-8e32-5fdd55ad13f0","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.982397Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:96c6d9dc411dc6130219cca864ef0f64fc213eec5d23ae9b6150560ae32933cd","observation_id":"7b516cce-b0dc-429f-b5f5-3d530901b578","resolution":{"observed_at":"2026-08-12T11:07:33.256431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:32.985846Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.985846Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:dd336f8a88ea95ba001d39ffb12dda41f956d9108904b4e1f1733b8c1b1a8942","observation_id":"9d1ad4e4-83f0-465a-bb02-e09a5873f03e","resolution":{"observed_at":"2026-08-12T11:07:32.985846Z","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":"2026-08-12T11:07:32.989579Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.989579Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:3a3438beeb582c5dbd72e3bfff27b8e4abb7eadd8159144bb8a305ef506892f1","observation_id":"6322d8af-77fa-4b47-a2de-b23749a87e70","resolution":{"observed_at":"2026-08-12T11:07:32.989579Z","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":"2026-08-12T11:07:33.228801Z","title":"Faceswap,","venue":null,"work_id":"1fbb2691-41d9-4dd6-84c6-338c44537813","year":2018},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.992914Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:a8aecd4fff9ca90c2389f9604e2f15ce6565eb177e619eb29b76d9b62a5e3016","observation_id":"aebba77d-f243-4105-b4f0-12453d0395de","resolution":{"observed_at":"2026-08-12T11:07:33.232314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T11:07:32.996297Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:32.996297Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:768c889bc3aa099a76eb979cae8a93cda6f8ec33a2fd25baa600e166d40fae60","observation_id":"35452609-7ba0-4799-bf44-b2e71f1d3406","resolution":{"observed_at":"2026-08-12T11:07:32.996297Z","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":"2026-08-12T11:07:33.000279Z","title":"Domain general face forgery detection by learning to weight,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.000279Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:7e09d760fed71de1759a0816065d9a10ca73936ef007da07632723bab0e2d5f7","observation_id":"37ddff39-d35d-4413-ba65-ec8ee8264a88","resolution":{"observed_at":"2026-08-12T11:07:33.000279Z","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":"2026-08-12T11:07:33.211945Z","title":"Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolu- tional network,","venue":null,"work_id":"552b8f2e-efd1-4bd1-8ae1-0ebecb5c79c5","year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.003618Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:08e81eeaa20e709ce66c928eab2f0d9e3a09ee38da6689e2e48f1325857df2b0","observation_id":"171e630c-7ad5-462d-bd78-4364c984961a","resolution":{"observed_at":"2026-08-12T11:07:33.215609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.201493Z","title":"Spatial-phase shallow learning: rethinking face forgery detection in frequency domain,","venue":null,"work_id":"6d59afb1-c811-4354-9d2f-0e406339724c","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.007186Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:7c2405dff5eae335e21d1611e51f2babd5a104701317ce78a564e7bb05b07c32","observation_id":"42d63b2c-e0f2-401d-b71a-0dad5c157579","resolution":{"observed_at":"2026-08-12T11:07:33.205377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.191408Z","title":"Dynamic inconsistency-aware deepfake video detection","venue":null,"work_id":"69fc159c-97b1-44a7-be3a-a4815dec3d5a","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.010759Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:8430f26f42ecd45ccc302a01060a589636a17ffcebaaf3f5c1b0c8ef9925ec0f","observation_id":"19946732-1e0a-45c8-bbda-8d95281c0608","resolution":{"observed_at":"2026-08-12T11:07:33.194913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:07:33.014102Z","title":"Finfer: Frame inference- based deepfake detection for high-visual-quality videos,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.014102Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:e8c78bbefd6aae6a80fe0915d91bc4d2fe559418b15f126175d9426aea24d4a4","observation_id":"1e59e07c-11f8-40a5-933b-74f34a5d4afd","resolution":{"observed_at":"2026-08-12T11:07:33.014102Z","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":"2026-08-12T11:07:33.172767Z","title":"Dsanet: Dynamic segment aggregation network for video-level representation learning,","venue":null,"work_id":"3bd90b71-0337-437e-8b59-b3c1bc1168ed","year":2021},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.017750Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:e4d4ab6238ec55640b2dac3af25f6462d8e99d83d29e255ffe4944a85fd9158a","observation_id":"af42b54a-2e0f-477d-b340-8c9e9af00b5b","resolution":{"observed_at":"2026-08-12T11:07:33.178109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.08854","last_updated":"2019-10-23T18:47:35Z","snapshot_observed_at":"2026-08-06T07:27:19.098848Z","submitted_at":"2019-10-19T22:35:52Z","title":"The Deepfake Detection Challenge (DFDC) Preview Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.08854","snapshot_observed_at":"2026-08-12T11:07:33.021109Z","title":"The deepfake detection challenge (dfdc) preview dataset,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.021109Z"},"links":{"cited_paper":"/paper/1910.08854","citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:2f9e38c39694d63b7384916bca2c26822066eca96b1a86b66697f35013762304","observation_id":"e71b0341-f741-4784-8b43-7a753f3515e8","resolution":{"observed_at":"2026-08-12T11:07:33.021109Z","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":"2026-08-12T11:07:33.024715Z","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T11:07:33.024715Z"},"links":{"citing_paper":"/paper/2411.18572"},"observation_digest":"sha256:5383d344b8c0389a4bbb5de88c4a2ae48523aef88c20a56300e09dcc99d8bd2f","observation_id":"655a2066-0507-448d-b6cf-b589b9aca699","resolution":{"observed_at":"2026-08-12T11:07:33.024715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.18572","last_updated":"2024-11-27T18:16:11Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T11:02:15.818833Z","submitted_at":"2024-11-27T18:16:11Z","title":"Exploring Depth Information for Detecting Manipulated Face Videos"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":62},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2411.18572."}