{"as_of":"2026-08-10T00:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8cf284f437dcd40fe033d4e5cdc9331e23418485399816abedd5c7bccaa7cd5a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":22,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T04:21:37.841003Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":19,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-07T14:31:12.205648Z","title":"Deepfakebench: A compre- hensive benchmark of deepfake detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18787","last_updated":"2025-06-17T22:31:49Z","snapshot_observed_at":"2026-08-07T23:01:54.979818Z","submitted_at":"2025-05-24T16:58:53Z","title":"Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:31:12.205648Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2505.18787"},"observation_digest":"sha256:a246a6d282891b4382e2f59def74256177a6650a7b0716d5b7d6d673d30ef7ad","observation_id":"e0336237-e0c3-4129-8b6f-0b543279a7b7","resolution":{"observed_at":"2026-08-07T14:31:12.205648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-07T10:45:47.064037Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.04501","last_updated":"2025-06-04T22:50:07Z","snapshot_observed_at":"2026-08-08T08:18:50.981706Z","submitted_at":"2025-06-04T22:50:07Z","title":"AuthGuard: Generalizable Deepfake Detection via Language Guidance","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T10:45:47.064037Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2506.04501"},"observation_digest":"sha256:b82d2a8f9bed1714bf4e50e6763cc3f309d81104fae77cfa93f681c488389027","observation_id":"00b0de51-3374-4f0f-9453-4d91fea9854b","resolution":{"observed_at":"2026-08-07T10:45:47.064037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-07T10:31:25.741646Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.ArXiv, abs/2307.01426, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05119","last_updated":"2025-06-05T15:06:16Z","snapshot_observed_at":"2026-08-10T00:07:30.058102Z","submitted_at":"2025-06-05T15:06:16Z","title":"Practical Manipulation Model for Robust Deepfake Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T10:31:25.741646Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2506.05119"},"observation_digest":"sha256:40cc9b8c54f29e1753adb217a2ab2fc2e094159259c3e5e826c390c5731b4875","observation_id":"e5fc4d4d-abe6-42a8-b157-1545d299e4e1","resolution":{"observed_at":"2026-08-07T10:31:25.741646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-06T19:15:56.458455Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05996","last_updated":"2025-07-08T13:54:48Z","snapshot_observed_at":"2026-08-09T06:31:44.185421Z","submitted_at":"2025-07-08T13:54:48Z","title":"Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:15:56.458455Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2507.05996"},"observation_digest":"sha256:8c95abf313def5f47ac2fe4be5f02095c8811e31407ea9ec7a924d9aa86569fc","observation_id":"1d0e947f-9ecd-47c9-b34b-6bb87d21b61c","resolution":{"observed_at":"2026-08-06T19:15:56.458455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T15:08:54.028967Z","title":"In: Advances in Ne ural Information Processing Systems (NeurIPS) Datasets and Benchmarks Trac k, vol","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20449","last_updated":"2025-08-28T05:55:28Z","snapshot_observed_at":"2026-08-09T01:02:49.855169Z","submitted_at":"2025-08-28T05:55:28Z","title":"A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T15:08:54.028967Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2508.20449"},"observation_digest":"sha256:55ef52b0ee62a5cd00c41cf0ff1b902aafe840dec4590cb99931d482e47b2b84","observation_id":"6f0d76aa-d474-4d0a-aeca-46ced4f23e3b","resolution":{"observed_at":"2026-08-05T15:08:54.028967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-03T20:51:00.128393Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.18436","last_updated":"2026-07-18T16:26:48Z","snapshot_observed_at":"2026-08-09T13:48:57.787727Z","submitted_at":"2025-11-23T13:09:02Z","title":"When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T20:51:00.128393Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2511.18436"},"observation_digest":"sha256:c7e07996423fe79b53fd8af0fe8c5068dd296a37a97640ca674b8de0c62b7eae","observation_id":"a0c555d6-117e-46e1-8dca-c65b07b5b639","resolution":{"observed_at":"2026-08-03T20:51:00.128393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2512.04331","last_updated":"2026-05-16T04:32:10Z","snapshot_observed_at":"2026-08-03T00:20:06.572788Z","submitted_at":"2025-12-03T23:40:12Z","title":"Open Set Face Forgery Detection via Dual-Level Evidence Collection","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-21T17:45:55.480425Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2512.04331"},"observation_digest":"sha256:90f2bb0fed06e5535f7a3d1cc4317ee36ea35d2da6d8e40dac86dab49ff0a2ac","observation_id":"b7d1d080-3060-4112-adbd-7a4ff3915fca","resolution":{"observed_at":"2026-05-21T17:50:26.409291Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":2},"reference_index":123,"source":"pdf_text","source_observed_at":"2026-05-17T00:29:07.951709Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:d14294f29e63fa2cfbc32e1f15d2846c1c6162209bb156735c4acdd54f0f5023","observation_id":"c13d39c1-1f49-46ef-8d48-e0e166589f28","resolution":{"observed_at":"2026-05-17T00:31:24.696262Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-03T18:19:21.189019Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":4},"reference_index":123,"source":"pdf_text","source_observed_at":"2026-08-03T18:19:21.189019Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:442a17bc6265b63edc3ed931174454c420752bcca93088af4dce7dab0a6fb9eb","observation_id":"79c47103-f518-4f56-b086-463246f5a08c","resolution":{"observed_at":"2026-08-03T18:19:21.189019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2601.01041","last_updated":"2026-04-11T15:54:44Z","snapshot_observed_at":"2026-07-31T19:28:22.370542Z","submitted_at":"2026-01-03T02:33:18Z","title":"Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-16T18:32:04.723901Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2601.01041"},"observation_digest":"sha256:bcbeb7f2681f051f2ac7568d2801e790e5c6518262801f466a59643f4a1d5100","observation_id":"d4f1c4f7-2fe2-4f02-bb93-369970c79c3d","resolution":{"observed_at":"2026-05-16T18:33:15.267828Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2604.14574","last_updated":"2026-04-16T03:06:23Z","snapshot_observed_at":"2026-07-06T23:02:18.425249Z","submitted_at":"2026-04-16T03:06:23Z","title":"M3D-Net: Multi-Modal 3D Facial Feature Reconstruction Network for Deepfake Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T11:18:27.502612Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2604.14574"},"observation_digest":"sha256:ae4b39b543a8c3b6eec13eb54e27169ac8c3259c80cd5b62da2e3ffd8a6a5445","observation_id":"a3d8713f-0756-410b-9ff8-b1ed8f0d78cd","resolution":{"observed_at":"2026-05-10T11:20:10.169312Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2605.17187","last_updated":"2026-05-16T22:52:11Z","snapshot_observed_at":"2026-07-06T23:28:12.114488Z","submitted_at":"2026-05-16T22:52:11Z","title":"PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-20T14:05:14.737146Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2605.17187"},"observation_digest":"sha256:6a769983ab380ace8beb3e657ab6a2b9b7bd159034391857c3314f2ddefb3317","observation_id":"e2a3b71f-9d48-4598-b4c9-434d20032bf5","resolution":{"observed_at":"2026-05-20T14:08:20.755257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2605.29092","last_updated":"2026-05-27T20:51:04Z","snapshot_observed_at":"2026-07-06T23:38:32.841875Z","submitted_at":"2026-05-27T20:51:04Z","title":"Lightweight Complementary-Cue Fusion for Robust Video Face Forgery Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T13:02:13.312764Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2605.29092"},"observation_digest":"sha256:23b6fc67bff35ffae0c48e7da204d3bf53f3477f6560fa7f94387353ecc0ff4d","observation_id":"3a7c5dd3-c7a2-4a82-b07e-37b83c1479c5","resolution":{"observed_at":"2026-06-29T13:03:25.866245Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2605.31192","last_updated":"2026-05-29T12:01:17Z","snapshot_observed_at":"2026-08-03T03:46:10.037632Z","submitted_at":"2026-05-29T12:01:17Z","title":"The Regularizing Power of Language-Training Deepfake Detectors","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-28T23:19:55.111556Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2605.31192"},"observation_digest":"sha256:302ab5d776526b659d50b22525f4afcbd84df095f2bb9e32416694d4d30f6a3e","observation_id":"f64d7076-2f94-4a37-beab-bb28a46b5f90","resolution":{"observed_at":"2026-06-28T23:22:46.802413Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2606.01843","last_updated":"2026-06-01T07:54:58Z","snapshot_observed_at":"2026-08-07T23:27:01.368080Z","submitted_at":"2026-06-01T07:54:58Z","title":"Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-28T15:12:34.221684Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2606.01843"},"observation_digest":"sha256:a1bf2e787482d59d0a918cc11947c597cb34b4816f4e2b5687dc3854c8b30e64","observation_id":"b990bcfb-192b-4a8d-8f71-aa7d03878dc6","resolution":{"observed_at":"2026-07-01T22:36:17.602602Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2606.03348","last_updated":"2026-06-02T08:57:38Z","snapshot_observed_at":"2026-08-01T10:55:46.652720Z","submitted_at":"2026-06-02T08:57:38Z","title":"SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-28T11:16:44.986901Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2606.03348"},"observation_digest":"sha256:0fd62e1a9ad9b9e3741182cb544ff6a55be228d140f76ae9d94d1c45329f5770","observation_id":"6351f99f-4835-4795-825b-d77970ea3115","resolution":{"observed_at":"2026-07-02T02:06:26.603228Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2606.09881","last_updated":"2026-06-03T05:44:29Z","snapshot_observed_at":"2026-07-06T23:49:08.412079Z","submitted_at":"2026-06-03T05:44:29Z","title":"Toward Calibrated, Fair, and accurate Deepfake Detection","version":1},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-06-28T07:05:18.026601Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2606.09881"},"observation_digest":"sha256:4a7e95b44d93ade6c47b818db98f0f3510c0a5b932ed51d5d0ed9b616cd8b9bd","observation_id":"71474591-3d84-4f33-9cbf-fe107f1f3fa8","resolution":{"observed_at":"2026-06-28T07:11:45.082670Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":"2307.01426","doi":"10.48550/arxiv.2307.01426","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426","venue":"arXiv (Cornell University)","work_id":"9367b811-36b8-44e1-a063-4f63ec14a52b","year":2023},"citing_paper":{"arxiv_id":"2606.26384","last_updated":"2026-06-24T21:10:42Z","snapshot_observed_at":"2026-08-02T18:27:05.790496Z","submitted_at":"2026-06-24T21:10:42Z","title":"What Do Deepfake Benchmarks Measure? An Audit Using Frozen Self-Supervised Representations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T01:24:46.200846Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2606.26384"},"observation_digest":"sha256:2d566a3dd476237178eda4f8487cc0965679e81845500caefae0fe6237f2db6b","observation_id":"c65d42b1-84e5-4368-b3ff-b4493c28019c","resolution":{"observed_at":"2026-07-04T15:49:57.133516Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:21.273903+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-07-12T03:54:48.803944Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.05434","last_updated":"2026-07-03T11:54:32Z","snapshot_observed_at":"2026-08-09T18:12:42.276494Z","submitted_at":"2026-07-03T11:54:32Z","title":"Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T03:54:48.803944Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2607.05434"},"observation_digest":"sha256:f3de76285475696aed699ef428941afeedc5ae4a0d599dd872098cfb2f31148a","observation_id":"3ae4f4d5-0608-4fc4-a406-18ef70ba3740","resolution":{"observed_at":"2026-07-12T03:54:48.803944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-01T19:47:44.410429Z","title":"arXiv preprint arXiv:2307.01426 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16873","last_updated":"2026-07-18T16:25:27Z","snapshot_observed_at":"2026-08-08T03:15:21.664094Z","submitted_at":"2026-07-18T16:25:27Z","title":"InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection","version":1},"reference_index":184,"source":"arxiv_source","source_observed_at":"2026-08-01T19:47:44.410429Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2607.16873"},"observation_digest":"sha256:2d84f80c92e50d818aef785a319468fcf6b50d2771c0ff4a360a3490c1ab73e3","observation_id":"5dc29476-4a53-4424-b16a-d6e1c53abd71","resolution":{"observed_at":"2026-08-01T19:47:44.410429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-06T00:24:41.871653Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.01334","last_updated":"2026-08-02T15:54:54Z","snapshot_observed_at":"2026-08-09T03:53:59.478499Z","submitted_at":"2026-08-02T15:54:54Z","title":"SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T00:24:41.871653Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2608.01334"},"observation_digest":"sha256:b21f0cae3dd2a6b45bbc15cb0401962d664152693588eb4ba647d222672d5dfb","observation_id":"d32bb66a-f829-4ade-8438-38b4c7a8a201","resolution":{"observed_at":"2026-08-06T00:24:41.871653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01426","snapshot_observed_at":"2026-08-08T04:21:37.841003Z","title":"arXiv preprint arXiv:2307.01426 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03008","last_updated":"2026-08-04T01:41:40Z","snapshot_observed_at":"2026-08-09T19:18:43.408273Z","submitted_at":"2026-08-04T01:41:40Z","title":"V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T04:21:37.841003Z"},"links":{"cited_paper":"/paper/2307.01426","citing_paper":"/paper/2608.03008"},"observation_digest":"sha256:5c1fdff6e0c6ad531856dd0eb87afe4dc4a351d8ef33e6db69ea49e867a9e122","observation_id":"61fbd09f-3a0f-4c4e-b13a-24338c46e3d7","resolution":{"observed_at":"2026-08-08T04:21:37.841003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2307.01426/citation-record","integrity":"/paper/2307.01426/integrity","json":"/paper/2307.01426/citation-record.json","paper":"/paper/2307.01426"},"outbound":[],"paper":{"arxiv_id":"2307.01426","last_updated":"2023-10-28T10:05:52Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T23:00:53.322063Z","submitted_at":"2023-07-04T01:34:41Z","title":"DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2307.01426."}