{"as_of":"2026-08-04T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f69b1422d695c9ecd2bcb0fb5a9e3f77361b39cad4e0ca23a3a9092d4a8faff2","coverage":[{"denominator":107,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T08:21:33.503188Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.06254/citation-record","integrity":"/paper/2607.06254/integrity","json":"/paper/2607.06254/citation-record.json","paper":"/paper/2607.06254"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T08:21:32.246644Z","title":"Finance worker pays out $25 million after video call with deepfake ‘chief financial officer’","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.246644Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:c3bea9eeecd61c71d4d61233f44d092e025f7b68ecd9675b51d888e4d316ba54","observation_id":"030d77ca-0610-4ac4-aa46-47d4338b537f","resolution":{"observed_at":"2026-08-02T08:21:32.246644Z","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":"2026-08-02T08:21:32.317657Z","title":"Berryessa","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.317657Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:b298070dbe8a0a3d60190fbb53d1f2aa98dbe72404e4caf437d113742b2741b2","observation_id":"d09661ee-2f08-41dc-89d5-6352fe43dff4","resolution":{"observed_at":"2026-08-02T08:21:32.317657Z","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":"2026-08-02T08:21:32.406045Z","title":"How AI is being abused to create child sexual abuse imagery","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.406045Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:60b6371da7270a5e4f87f3d7f2c8fc7f43b5ea1acab604f8435f7c713b409de1","observation_id":"ac77e371-fdc9-401e-a5c4-c3f39953ecfa","resolution":{"observed_at":"2026-08-02T08:21:32.406045Z","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":"2026-08-02T08:21:32.540988Z","title":"Misinformation and elections: Provisional findings from four countries in 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.540988Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:8f956c57ac096c78e7a2b9761e556dc93bddcf3111c9a9316ee6f26475cb28d3","observation_id":"e57dc6da-70cd-482c-ba1f-e2f22fde8daf","resolution":{"observed_at":"2026-08-02T08:21:32.540988Z","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":"2026-08-02T08:21:32.712775Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.712775Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:5449b727cd297e20477f5a5d8add115a2efb20c0e9bb95a6d83d09423051daf1","observation_id":"5473028e-4324-4ee6-8c30-157f5ea62a02","resolution":{"observed_at":"2026-08-02T08:21:32.712775Z","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":"2026-08-02T08:21:32.848488Z","title":"Gemini: A family of highly capable multimodal models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.848488Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f96748657006a9c589cfed0afcf0556908ff5b90d186a374ca80d4da62ddf603","observation_id":"67d6afb7-2c78-48be-bb7a-756e303b6780","resolution":{"observed_at":"2026-08-02T08:21:32.848488Z","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":"2026-08-02T08:21:32.968686Z","title":"Drct: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:32.968686Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:05b55502ecdd018a54f8d7af2fbbf5731a226e29a6b7e99705afb7351afb1fd7","observation_id":"77332b90-fde5-427f-8c60-9d67c8a70350","resolution":{"observed_at":"2026-08-02T08:21:32.968686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19091","last_updated":"2024-07-08T13:20:16Z","snapshot_observed_at":"2026-07-06T17:37:21.849767Z","submitted_at":"2024-02-29T12:18:43Z","title":"Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19091","snapshot_observed_at":"2026-08-02T08:21:33.060784Z","title":"Leveraging representations from intermediate encoder-blocks for synthetic image detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.060784Z"},"links":{"cited_paper":"/paper/2402.19091","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:bc3f8912cc014b9c0e41603581940c23a30dcf9d104f404b95ecee161018e8a7","observation_id":"f4fd8e9b-1534-4940-b598-66d617613988","resolution":{"observed_at":"2026-08-02T08:21:33.060784Z","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":"2026-08-02T08:21:33.210981Z","title":"Deepfake-eval- 2024: A multi-modal in-the-wild benchmark of deepfakes circulated in 2024, 2025","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.210981Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:4d5fc0741b47227bc0e4207c5efee7904a2e8199a2d9dec105f405b3327eba81","observation_id":"3a18d7eb-a2f9-42ae-8cb0-6af82fb4f23d","resolution":{"observed_at":"2026-08-02T08:21:33.210981Z","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":"2026-08-02T08:21:33.243328Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.243328Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:1d84c86f17342987cfe3c036134cb77fed832fd49ccec699ed37405a2b9cc35d","observation_id":"7135c9b2-028f-4684-ae67-8fafb77ad908","resolution":{"observed_at":"2026-08-02T08:21:33.243328Z","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":"2026-08-02T08:21:33.245985Z","title":"FaceForensics++: Learning to detect manipulated facial images","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.245985Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3b1bfb2c59960d28f00812aabb45a57bd6d64f8a105f5777b12c42754d5acbd9","observation_id":"3a314576-a019-41f8-b239-856647c431fc","resolution":{"observed_at":"2026-08-02T08:21:33.245985Z","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":"2026-08-02T08:21:33.249176Z","title":"GenImage: A million-scale benchmark for detecting AI-generated image","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.249176Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:24be0d966730d28ad4c388567dddd3603bb6bc501706b97cc8414221dbe5e971","observation_id":"f96e5cc4-ceb9-4704-bbb9-71cec3e2a9f4","resolution":{"observed_at":"2026-08-02T08:21:33.249176Z","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":"2026-08-02T08:21:33.252093Z","title":"The state of deepfakes: Landscape, threats, and impact","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.252093Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:cf2c0b0fc48e6227313d8fbe0c79275222c0300b164f99baee3d9a92b3022c58","observation_id":"1781cf07-6c07-43a8-88c9-805422f79877","resolution":{"observed_at":"2026-08-02T08:21:33.252093Z","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":"2026-08-02T08:21:33.254445Z","title":"The 2023 state of deepfakes: Realities, threats, and impact, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.254445Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:6e76abbea866bd62ee570097c0ee46493476aad0fa2240a18110fbf8ceb422e8","observation_id":"5bc7d6af-e145-4c4f-ae92-87df6876db8b","resolution":{"observed_at":"2026-08-02T08:21:33.254445Z","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":"2026-08-02T08:21:33.257154Z","title":"Fincen alert on fraud schemes involving deepfake media targeting financial institutions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.257154Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:44cb5dedf44ed8d96ed11fd54d400f2d90284c83a07da549b4fa9d8458ee1770","observation_id":"dbfc4b7d-4fe5-4799-97b2-cdc5ad969686","resolution":{"observed_at":"2026-08-02T08:21:33.257154Z","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":"2026-08-02T08:21:33.271058Z","title":"Global cybersecurity outlook 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.271058Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f0ef1e96c055595c581a5939c29f6c98582924ed2fe2b0904ce93ee49e0278a6","observation_id":"58baf44a-6813-4bf8-8b84-4959e9fe0103","resolution":{"observed_at":"2026-08-02T08:21:33.271058Z","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":"2026-08-02T08:21:33.274230Z","title":"Children and deepfakes","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.274230Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:1c903e155b32f6d99e6f9aa979127ebaa6c9e1aa46002e68cfc24d20eee0d346","observation_id":"c04c6ec4-70aa-4340-bfbc-f412612473d0","resolution":{"observed_at":"2026-08-02T08:21:33.274230Z","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":"2026-08-02T08:21:33.276861Z","title":"Unmasking cybercrime: Strengthening digital identity verification against deepfakes, January 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.276861Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:30bf335a65b07dca31917acf6db04af3939c270a1dc7fa20f50b34d71307b131","observation_id":"53ffab6f-2925-44b9-b5e3-a87ae09a4f70","resolution":{"observed_at":"2026-08-02T08:21:33.276861Z","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":"2026-08-02T08:21:33.279504Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.279504Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:387dec07a5042506668aabb20cf55b766b26f6c6e101da46e4383c581136ebaa","observation_id":"173bddd2-1fed-4f1b-b060-3c76d5176c46","resolution":{"observed_at":"2026-08-02T08:21:33.279504Z","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":"2026-08-02T08:21:33.282144Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.282144Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:bd4ad41d26c674d70cc4190934b6bdf0df5838a8f494759a9c903e20d365630d","observation_id":"51074500-10e6-4221-9808-f0e1a9280b1b","resolution":{"observed_at":"2026-08-02T08:21:33.282144Z","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":"2026-08-02T08:21:33.284871Z","title":"Analyzing and improving the image quality of StyleGAN","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.284871Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:7cf27fbdbcafc54effd6fc00d8436032b5f8d62334571c85f99acafee708cabf","observation_id":"e3b8e659-0a40-439d-ac69-a91b208a9e06","resolution":{"observed_at":"2026-08-02T08:21:33.284871Z","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":"2026-08-02T08:21:33.287399Z","title":"Jain, and Pieter Abbeel","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.287399Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:b2ab9e8cf2d513edc59a86dda596f0a35c989ed1d08f49ce02da7005315dc517","observation_id":"71d27f89-b298-4289-a889-7e69a076d8a4","resolution":{"observed_at":"2026-08-02T08:21:33.287399Z","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":"2026-08-02T08:21:33.290026Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.290026Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f5f8d879404176ae523267e53c73ac33369f7441bd2999486c9b362bcdeea1bc","observation_id":"8ef252e9-11f6-4375-a824-5e8cfb494147","resolution":{"observed_at":"2026-08-02T08:21:33.290026Z","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":"2026-08-02T08:21:33.292828Z","title":"Fast face-swap using convolutional neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.292828Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:a865008136f6dda77bd1948741757f83024820c7df66426243cc36e7baea7d99","observation_id":"b13b75a2-70ca-4533-bcad-d89246f65394","resolution":{"observed_at":"2026-08-02T08:21:33.292828Z","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":"2026-08-02T08:21:33.295496Z","title":"Face2face: Real-time face capture and reenactment of RGB videos","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.295496Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f967b963ae3f1678c7dd80a8fdba7a8480a47bdca6bb49b62b97a106fa49d88b","observation_id":"20fef3db-ddfc-48bd-a10d-e85eca78a3ed","resolution":{"observed_at":"2026-08-02T08:21:33.295496Z","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":"2026-08-02T08:21:33.298311Z","title":"Deferred neural rendering: Image synthesis using neural textures.ACM Transactions on Graphics (TOG), 38(4):66:1–66:12, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.298311Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:35771391dc652ca1d4272521e97e490d47c99e894534d34739227204484b203c","observation_id":"0169c81b-342e-4ca9-bd33-023276ba1417","resolution":{"observed_at":"2026-08-02T08:21:33.298311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08685","last_updated":"2018-12-20T16:36:39Z","snapshot_observed_at":"2026-08-03T08:33:32.535153Z","submitted_at":"2018-12-20T16:36:39Z","title":"DeepFakes: a New Threat to Face Recognition? Assessment and Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08685","snapshot_observed_at":"2026-08-02T08:21:33.300927Z","title":"Deepfakes: a new threat to face recognition? assessment and detection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.300927Z"},"links":{"cited_paper":"/paper/1812.08685","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:8d97748ce4b1c6eb40f325ed7eb4fd0bf53c54046c8273d59b821760a41d7ea9","observation_id":"b131f12e-23de-4bcd-a56e-2c7d431169f8","resolution":{"observed_at":"2026-08-02T08:21:33.300927Z","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":"2026-08-02T08:21:33.303788Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.303788Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:d44a8b3e6a66ef8155961ed6a7aada4003fe7570abca5e8a72d58d59f0b8a483","observation_id":"c0824274-ded3-46d1-b227-d9b3d751733f","resolution":{"observed_at":"2026-08-02T08:21:33.303788Z","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":"2026-08-02T08:21:33.306571Z","title":"Leveraging frequency analysis for deep fake image recognition","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.306571Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:7ae8dbd30839527aa649c7ae4c2548a15c3bc32e5df307687c0d07ac1b33493e","observation_id":"d4abc40b-5e6e-4b93-898a-ff90baaa04f1","resolution":{"observed_at":"2026-08-02T08:21:33.306571Z","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":"2026-08-02T08:21:33.309262Z","title":"Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.309262Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:86fdf32603a05c1296f4edcf997b5e14f60f821a80c3086329718918a1403322","observation_id":"b13aeec7-f8ce-4af4-8bd5-31b9b8e3e5ba","resolution":{"observed_at":"2026-08-02T08:21:33.309262Z","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":"2026-08-02T08:21:33.311499Z","title":"Towards universal fake image detectors that generalize across generative models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.311499Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:ee5afbf4498dffd8aca0bee883adbbeea8a7b5e4bf565f72623c033cf3d370f0","observation_id":"b8a74e71-b743-43a6-b5f7-e0dbd7ad3ec2","resolution":{"observed_at":"2026-08-02T08:21:33.311499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00195","last_updated":"2024-04-29T14:25:42Z","snapshot_observed_at":"2026-07-06T16:55:20.926830Z","submitted_at":"2023-11-30T21:11:20Z","title":"Raising the Bar of AI-generated Image Detection with CLIP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00195","snapshot_observed_at":"2026-08-02T08:21:33.314168Z","title":"Raising the bar of AI-generated image detection with CLIP.arXiv preprint arXiv:2312.00195, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.314168Z"},"links":{"cited_paper":"/paper/2312.00195","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:9b57f0e72c5111bafa666c38fe83d76f0f0b8b48373d7c30c9437c403933dbc1","observation_id":"fe0c60bd-3cae-40c6-9e43-b143ac855d0e","resolution":{"observed_at":"2026-08-02T08:21:33.314168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.09295","last_updated":"2023-03-16T13:15:03Z","snapshot_observed_at":"2026-07-06T15:04:11.708950Z","submitted_at":"2023-03-16T13:15:03Z","title":"DIRE for Diffusion-Generated Image Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.09295","snapshot_observed_at":"2026-08-02T08:21:33.317087Z","title":"DIRE for diffusion-generated image detection.arXiv preprint arXiv:2303.09295, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.317087Z"},"links":{"cited_paper":"/paper/2303.09295","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:8739845d548925abed6c08a1d92bc7845454f2c978703cf70dfe22dc4a2af890","observation_id":"e5ced537-2270-440f-9edc-079cb93e9d55","resolution":{"observed_at":"2026-08-02T08:21:33.317087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17879","last_updated":"2024-03-27T09:17:14Z","snapshot_observed_at":"2026-07-06T17:23:05.927644Z","submitted_at":"2024-01-31T14:36:49Z","title":"AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17879","snapshot_observed_at":"2026-08-02T08:21:33.319755Z","title":"AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error.arXiv preprint arXiv:2401.17879, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.319755Z"},"links":{"cited_paper":"/paper/2401.17879","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:17f8e265a267bddc23fa6f1d35de7d72a3d0863851a314bee84e7bf52325e91a","observation_id":"2106b688-caf2-4925-bdfa-54ada07b9a81","resolution":{"observed_at":"2026-08-02T08:21:33.319755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09647","last_updated":"2024-12-05T14:06:14Z","snapshot_observed_at":"2026-07-06T19:02:11.267510Z","submitted_at":"2024-08-19T02:14:25Z","title":"C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09647","snapshot_observed_at":"2026-08-02T08:21:33.322777Z","title":"C2p-clip: Injecting cate- gory common prompt in clip to enhance generalization in deepfake detection.arXiv preprint arXiv:2408.09647,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.322777Z"},"links":{"cited_paper":"/paper/2408.09647","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f7eeeabecc4721877d19212b4e03fca73045ac04e75de47cbe5e13f13b21906c","observation_id":"ba6431a0-0600-46aa-ab0d-b32d6607c248","resolution":{"observed_at":"2026-08-02T08:21:33.322777Z","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":"2026-08-02T08:21:33.328714Z","title":"FaceForensics: A large-scale video dataset for forgery detection in human faces","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.328714Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:501a3daaf245f0fbbfe6b797ed5e41e26652a62d3e399e6bce8a996833cd7c9f","observation_id":"df04d543-bc25-4063-aa8d-2c2946c6258f","resolution":{"observed_at":"2026-08-02T08:21:33.328714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.08854","last_updated":"2019-10-23T18:47:35Z","snapshot_observed_at":"2026-07-06T08:30:47.148345Z","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":"2026-08-02T08:21:33.331303Z","title":"The Deepfake detection challenge (DFDC) preview dataset.arXiv preprint arXiv:1910.08854, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.331303Z"},"links":{"cited_paper":"/paper/1910.08854","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3a13acb8fd2a22856a7d7367e21d631125559f7c39f560bf04f154b1f8a9934e","observation_id":"170674bf-413b-4be1-8fc1-8eb6973a2db1","resolution":{"observed_at":"2026-08-02T08:21:33.331303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07397","last_updated":"2020-10-28T03:48:28Z","snapshot_observed_at":"2026-07-06T09:28:36.954658Z","submitted_at":"2020-06-12T18:15:55Z","title":"The DeepFake Detection Challenge (DFDC) Dataset","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07397","snapshot_observed_at":"2026-08-02T08:21:33.334289Z","title":"The DeepFake detection challenge (DFDC) dataset.arXiv preprint arXiv:2006.07397, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.334289Z"},"links":{"cited_paper":"/paper/2006.07397","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:7a69105c530dc3b29b24633e64b2898ea5a3e124b7fe8b7fa82106ab448cd541","observation_id":"ae5462c8-ac04-48e7-9a08-4ebd9cccac85","resolution":{"observed_at":"2026-08-02T08:21:33.334289Z","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":"2026-08-02T08:21:33.337477Z","title":"WildDeepfake: A challenging real- world dataset for deepfake detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.337477Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:701599445f55623efc96d47f9742453945972710956bc818eec37b22186fa06b","observation_id":"8e672458-88cc-4a38-a9b2-b38f5b15430b","resolution":{"observed_at":"2026-08-02T08:21:33.337477Z","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":"2026-08-02T08:21:33.340052Z","title":"Celeb-DF: A large-scale challenging dataset for DeepFake forensics","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.340052Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:17dc5d4cd244e172b65e92e8959b0fd5dfc57bf6a51bd81802e2f9304fe3ba30","observation_id":"db1c4728-b132-4d26-aa91-ee8353ad3d74","resolution":{"observed_at":"2026-08-02T08:21:33.340052Z","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":"2026-08-02T08:21:33.342760Z","title":"ForgeryNet: A versatile benchmark for comprehensive forgery analysis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.342760Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:d4b23a9685bd88d874fd3ee6a39150c49773376c8ce76c1b9ae7b78dc939a6d1","observation_id":"e2b1323d-6fd4-4178-9ddf-a3e11b0447ac","resolution":{"observed_at":"2026-08-02T08:21:33.342760Z","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":"2026-08-02T08:21:33.345369Z","title":"DF40: Toward next-generation deepfake detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.345369Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:eed95a05a55f535ad8eee58867e3aef99309304d2544949fecdb8818ac73c376","observation_id":"d2293183-c1a1-4eb5-993e-711b0f453a3d","resolution":{"observed_at":"2026-08-02T08:21:33.345369Z","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":"2026-08-02T08:21:33.348137Z","title":"Deepfakebench: A comprehensive benchmark of deepfake detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.348137Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3566bcbb740e0e4e4feb269eacafb7c8b091c29c2680c70b6e85e636027d6f34","observation_id":"8fb3dce8-b769-46c3-8f6b-da2b9dd26f33","resolution":{"observed_at":"2026-08-02T08:21:33.348137Z","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":"2026-08-02T08:21:33.350880Z","title":"Claude opus 4.8 model card, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.350880Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:6a20cdb6ef98c0ba3d04ddc50e429d9462ac411bbed8390b6e59ffa69726fe76","observation_id":"d83dd0b0-e22a-42f7-942f-a51a124b564b","resolution":{"observed_at":"2026-08-02T08:21:33.350880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12966","last_updated":"2023-10-13T02:41:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-24T17:59:17Z","title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12966","snapshot_observed_at":"2026-08-02T08:21:33.353966Z","title":"Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond.arXiv preprint arXiv:2308.12966, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.353966Z"},"links":{"cited_paper":"/paper/2308.12966","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:566993c95c4f80654fd41824c1e77ba1d9d26b46368fc1b76ab31906f0c60b9b","observation_id":"3ec7b99c-7f34-4639-9430-c5239ff18abd","resolution":{"observed_at":"2026-08-02T08:21:33.353966Z","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":"2026-08-02T08:21:33.357032Z","title":"Llama 4: Multimodal intelligence, openly available, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.357032Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:50df626e3c2d1e7c9cb7560601d90356b7e47c221889382a1a3a570c10f92fac","observation_id":"d5e2d9cd-ed0d-48f0-9966-4ac2ecbba9d4","resolution":{"observed_at":"2026-08-02T08:21:33.357032Z","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":"2026-08-02T08:21:33.359528Z","title":"Nemotron: Nvidia’s family of open foundation models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.359528Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:cc174f0bd3137dd754c06fe84895b1487af1e8716af7ed82ad6801d7f9d149af","observation_id":"a50d7bcc-685a-4b4f-9094-2c7c4d10b66c","resolution":{"observed_at":"2026-08-02T08:21:33.359528Z","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":"2026-08-02T08:21:33.362061Z","title":"Glm: General language model family (zhipu ai / z.ai), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.362061Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:ba283801f2e646be56b78a20bdd8a9d390f9f2007618d559690e678db34d1265","observation_id":"472d8a26-cbc0-4310-b3c9-ef7cc4dcb178","resolution":{"observed_at":"2026-08-02T08:21:33.362061Z","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":"2026-08-02T08:21:33.364777Z","title":"C2PA technical specification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.364777Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:97dd85858055c5de5ad4f2150595b7c6a6d14baef19464e3013cd0d74e377ca8","observation_id":"e55a12e0-ca5b-4007-81ce-3785a22a0a95","resolution":{"observed_at":"2026-08-02T08:21:33.364777Z","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":"2026-08-02T08:21:33.367344Z","title":"Content credentials: C2PA technical specification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.367344Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:0dabf6b7e942b1473b33be4d95671817d53fb3f7b82f2df3c292dfc3aef1a9c0","observation_id":"548a4aa1-f93b-42ee-a8d9-686b3b71e448","resolution":{"observed_at":"2026-08-02T08:21:33.367344Z","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":"2026-08-02T08:21:33.369676Z","title":"Scalable watermarking for identifying large language model outputs.Nature, 634(8035):818–823, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.369676Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:df35aa6b976610fcae2679e3b36db305ce4308589f3001f1445572920f63626b","observation_id":"875e60ec-b898-4f96-9684-9169452d91b4","resolution":{"observed_at":"2026-08-02T08:21:33.369676Z","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":"2026-08-02T08:21:33.372527Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.372527Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fc12d2c5214e68f054f57f7fd3a92d76daabc2254ff9995631cbb09df6b76f3e","observation_id":"2159425a-469f-4786-baf8-556b211d3f94","resolution":{"observed_at":"2026-08-02T08:21:33.372527Z","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":"2026-08-02T08:21:33.374911Z","title":"RealAPI product documentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.374911Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:ca71687a05e4068b47ecc749b3c2fd18d30631808c4c403f08a83d7bd83a29fb","observation_id":"079f588c-7f0f-44b7-ab08-6b209174d60f","resolution":{"observed_at":"2026-08-02T08:21:33.374911Z","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":"2026-08-02T08:21:33.377225Z","title":"Ai-generated image and video detection documentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.377225Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:4447fec49a9ce4e94393980b55a4cb8fd7a7bfbf43cb5d6ea0ad3582d9a2737b","observation_id":"b5ff5480-582d-469b-b9e3-854ab8bc1b14","resolution":{"observed_at":"2026-08-02T08:21:33.377225Z","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":"2026-08-02T08:21:33.379551Z","title":"Ai-generated image detection API documentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.379551Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:2eb9529dae1b15260dfbba01615f52a87337e1b6ba2a396d3034ef245e8ff19d","observation_id":"b692aac2-32fb-4050-b1a7-d21608879896","resolution":{"observed_at":"2026-08-02T08:21:33.379551Z","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":"2026-08-02T08:21:33.381882Z","title":"Ai image detection API documentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.381882Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:67ef9a63f656833ab0a736a439c733bc1c2b98d860ff9c38dedeffdb41297c32","observation_id":"f0504847-f41f-4cc8-83cd-7b5944271ab3","resolution":{"observed_at":"2026-08-02T08:21:33.381882Z","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":"2026-08-02T08:21:33.384257Z","title":"Ai generated image detection (DeepScan API)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.384257Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:c591fe3775c5ed1e86ae53e4aeaecfdad3e399932779a3bfa97b23d022fb3fe0","observation_id":"3ac50b06-d716-4ac9-9f78-ba21ad1cd6ee","resolution":{"observed_at":"2026-08-02T08:21:33.384257Z","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":"2026-08-02T08:21:33.386743Z","title":"Deepfake detection platform.https://sensity.ai/, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.386743Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:ab21d5e0c4207497632f8982834ce67a75d4df6ebd9928070218da1f054615e5","observation_id":"a1deeba8-39c8-4fdc-915a-6e9db175db88","resolution":{"observed_at":"2026-08-02T08:21:33.386743Z","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":"2026-08-02T08:21:33.388965Z","title":"Ai generated image detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.388965Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:bc756f01b059525f167dfca4527a1f47c900b8c1cb378ca037884bc3311a9a60","observation_id":"6d7e3336-e28e-4d28-89bd-3770b7150dc0","resolution":{"observed_at":"2026-08-02T08:21:33.388965Z","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":"2026-08-02T08:21:33.391634Z","title":"Image detection API reference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.391634Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:d2ca369b066442e6e9e8a608f64d6918e03248ac0794afc13f9ad65d3bc55dd1","observation_id":"6d12da17-db9f-4d6b-885f-09ed80a09e54","resolution":{"observed_at":"2026-08-02T08:21:33.391634Z","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":"2026-08-02T08:21:33.393940Z","title":"Intel introduces real-time deepfake detector (FakeCatcher)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.393940Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:977639c2d45d57a32ab4254bd7c66f91c0e86611b8ccecd4fcbbba7646133877","observation_id":"ecb7571f-d14b-4949-b8f6-83d86a079bf8","resolution":{"observed_at":"2026-08-02T08:21:33.393940Z","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":"2026-08-02T08:21:33.396382Z","title":"Poskitt, and Xingmei Wang","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.396382Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fc21c726da9b17a61e7e9a4ccc6ce7adca759fc038ffb80288880a0664cdf0dd","observation_id":"98e115fd-aa0a-4faf-b3f4-6a2cf6fd7126","resolution":{"observed_at":"2026-08-02T08:21:33.396382Z","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":"2026-08-02T08:21:33.398756Z","title":"Assessment framework for deepfake detection in real-world situations, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.398756Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:1ca529792a81429a3666d10d0e46c1f52c3ebe257bd30642c957a0e7e946cf53","observation_id":"2e378072-dbf5-4a93-9501-d0b7d105a9c7","resolution":{"observed_at":"2026-08-02T08:21:33.398756Z","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":"2026-08-02T08:21:33.401009Z","title":"Fake or JPEG? revealing common biases in generated image detection datasets, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.401009Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:eb831d51483a6f4eed52bcb71fb189679b3371d2c5830cf9d037deffb392625f","observation_id":"7ff2333e-9d72-4d20-a3c2-7e21a2ea08af","resolution":{"observed_at":"2026-08-02T08:21:33.401009Z","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":"2026-08-02T08:21:33.403513Z","title":"A sanity check for AI-generated image detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.403513Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:9bd71f093cbd7b64fc234b6c45e35d593d6330f1b0a17df0b086eff3d8d547b3","observation_id":"32faa44e-bfd5-431e-86a2-ebdf648253cf","resolution":{"observed_at":"2026-08-02T08:21:33.403513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14581","last_updated":"2024-04-22T21:00:13Z","snapshot_observed_at":"2026-07-31T23:08:43.403785Z","submitted_at":"2024-04-22T21:00:13Z","title":"The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14581","snapshot_observed_at":"2026-08-02T08:21:33.407003Z","title":"The adversarial AI-Art: Understanding, generation, detection, and benchmarking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.407003Z"},"links":{"cited_paper":"/paper/2404.14581","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:343998796a9b2e9f9baf5a47960fbf24c3b33751c384ad73f694c1918a49577f","observation_id":"36732663-2f4f-4312-a252-276882d45072","resolution":{"observed_at":"2026-08-02T08:21:33.407003Z","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":"2026-08-02T08:21:33.409948Z","title":"Organic or diffused: Can we distinguish human art from AI-generated images?https://doi.org/10.1145/3658644.3670306, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.409948Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:8dad324bf1c950c9a2b19e27b3ba5491e2321510c3a9bf2d37ffd94d7f8bea69","observation_id":"0f8cbe91-ecd7-42c6-87d7-883d682b9e26","resolution":{"observed_at":"2026-08-02T08:21:33.409948Z","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":"2026-08-02T08:21:33.412914Z","title":"The visual counter turing test (VCT2): A benchmark for evaluating AI-Generated image detection and the visual AI index.https://arxiv.org/abs/2411.16754, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.412914Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:4eab02acf4d76be9cf57517fc955c1441129d5b7685a29fe42d9845eb64d92b2","observation_id":"3b976128-531e-4785-aaa1-977e199ee8d4","resolution":{"observed_at":"2026-08-02T08:21:33.412914Z","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":"2026-08-02T08:21:33.415803Z","title":"Ai-genbench: A new ongoing benchmark for ai-generated image detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.415803Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:6e3bb57a9890ddd671e867efe415842eebd06c4c09f2b101ac4d202c646f58e5","observation_id":"28dd4218-b5e8-4411-895b-a0fb63aed43f","resolution":{"observed_at":"2026-08-02T08:21:33.415803Z","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":"2026-08-02T08:21:33.418448Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.418448Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:de2c00cafaef26eba4102933e53a27e9af853279fa93aa3b7b221ba4e2d7c976","observation_id":"18dee6c1-8db5-4731-981c-ee5ee2af1edc","resolution":{"observed_at":"2026-08-02T08:21:33.418448Z","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":"2026-08-02T08:21:33.421082Z","title":"Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.421082Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:804ff6ae209a811c7fac4a07ec1d3628927deb115550a4f854eac140c33be714","observation_id":"954d88b6-7071-4902-9f07-59816409a0a7","resolution":{"observed_at":"2026-08-02T08:21:33.421082Z","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":"2026-08-02T08:21:33.423812Z","title":null,"venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.423812Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:62bd4e6cca22dbd7def61bc8599602f29a3219ba4468daa3b685bc335d306b4e","observation_id":"3ca11ec9-51d0-43c2-86c3-9f62a7035f8d","resolution":{"observed_at":"2026-08-02T08:21:33.423812Z","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":"2026-08-02T08:21:33.426523Z","title":"On the foundations of noise-free selective classification.Journal of Machine Learning Research, 11(53):1605–1641, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.426523Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:e4093bde2d8224087ceed93b2b2c35a64b0230702501cf98155337460a19d5e4","observation_id":"13b0b637-b54e-4ae6-8ac8-04727dabdfbe","resolution":{"observed_at":"2026-08-02T08:21:33.426523Z","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":"2026-08-02T08:21:33.429285Z","title":"Selective classification for deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.429285Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:1055abb0f0460f6a2fdbfc2b6a5a170bdb3106413abd3a9c3c8ae21debdbedf2","observation_id":"c41f9d44-e185-47b4-999b-44d37845bd73","resolution":{"observed_at":"2026-08-02T08:21:33.429285Z","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":"2026-08-02T08:21:33.431922Z","title":"SelectiveNet: A deep neural network with an integrated reject option","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.431922Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:db4f4e51b2caa360994769ae1025ad526406fd462eaf9297e1f4b2a14ebab83b","observation_id":"35e8f2b9-1aa8-4667-8aee-63b80504119b","resolution":{"observed_at":"2026-08-02T08:21:33.431922Z","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":"2026-08-02T08:21:33.435054Z","title":"Learning and evaluating classifiers under sample selection bias","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.435054Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fb9ea3f60b72d7485c384cb0a6f8a530c3c089b588a69c6842000c42c93a5cab","observation_id":"e7bcfee2-361a-40dc-a5a5-5eb9f1f33134","resolution":{"observed_at":"2026-08-02T08:21:33.435054Z","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":"2026-08-02T08:21:33.438107Z","title":"Counterfactually comparing abstaining classifiers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.438107Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:a9ae5e9b390d33ed36469f328e6430bd9aa98d4ca3bfc0c65f1bdf7e6bc678c4","observation_id":"14761f24-edd1-4d7f-b44a-c5b367f41f88","resolution":{"observed_at":"2026-08-02T08:21:33.438107Z","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":"2026-08-02T08:21:33.440738Z","title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC Genomics, 21(1):6, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.440738Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:194a77e4c34dbc112d5d40f22449038f6ce4a9284111f54ca3cc2352d12b4fe2","observation_id":"03034c77-d16b-4a09-941f-09791fc02614","resolution":{"observed_at":"2026-08-02T08:21:33.440738Z","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":"2026-08-02T08:21:33.443396Z","title":"Brodersen, Cheng Soon Ong, Klaas E","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.443396Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:1e6e5174953ad231bca0bb3cbe0099b436c8bc33ec79e0a093c86e4c2174fa8a","observation_id":"dbd28847-368d-481b-818b-0d2dc57f0167","resolution":{"observed_at":"2026-08-02T08:21:33.443396Z","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":"2026-08-02T08:21:33.445879Z","title":"A systematic analysis of performance measures for classification tasks","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.445879Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3fb64550f178fce4a1b76dc53a66330f9d5ac80d56723ae2a0d12c0082a49ac7","observation_id":"92db9387-99a6-4e54-bf1d-ef4515983c74","resolution":{"observed_at":"2026-08-02T08:21:33.445879Z","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":"2026-08-02T08:21:33.448579Z","title":"Declip: Decoupled learning for detection and localization of deepfakes","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.448579Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:2f3f40f08d0ee8cc8bbef07aac67abde745a69b6670cc2cba6fd06ea9d523ebb","observation_id":"2f998e30-b6e2-44c2-b04d-a21d82dc3b25","resolution":{"observed_at":"2026-08-02T08:21:33.448579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19435","last_updated":"2025-02-15T16:01:36Z","snapshot_observed_at":"2026-07-06T18:38:09.490968Z","submitted_at":"2024-06-27T17:59:49Z","title":"A Sanity Check for AI-generated Image Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19435","snapshot_observed_at":"2026-08-02T08:21:33.451201Z","title":"Aide: Frequency-forensic and semantic hybrid detection of ai-generated images","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.451201Z"},"links":{"cited_paper":"/paper/2406.19435","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:b57f83a303256fd75a67aec8db3db91209a167e48b7d81504df77dbb2e0c4079","observation_id":"00836d49-0b6e-4c14-9290-2533b863f893","resolution":{"observed_at":"2026-08-02T08:21:33.451201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.06248","last_updated":"2026-05-11T10:15:54Z","snapshot_observed_at":"2026-07-06T22:09:57.861336Z","submitted_at":"2025-08-08T12:03:56Z","title":"Deepfake Detection that Generalizes Across Benchmarks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.06248","snapshot_observed_at":"2026-08-02T08:21:33.454086Z","title":"Gend: Generalizable deepfake detection with dinov3-large","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.454086Z"},"links":{"cited_paper":"/paper/2508.06248","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:6f9a7f07038c44f482b592e3da77b4f170c6392eb91c815817f0fd4885b82392","observation_id":"d8e62703-7f3e-4ee3-8270-41066cca06ff","resolution":{"observed_at":"2026-08-02T08:21:33.454086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04125","last_updated":"2025-06-09T18:48:40Z","snapshot_observed_at":"2026-08-02T17:17:50.380968Z","submitted_at":"2024-11-06T18:59:41Z","title":"Community Forensics: Using Thousands of Generators to Train Fake Image Detectors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04125","snapshot_observed_at":"2026-08-02T08:21:33.457422Z","title":"Community forensics: Using a large-scale, diverse generator set to train fake image detectors, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.457422Z"},"links":{"cited_paper":"/paper/2411.04125","citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:e65bc6b5db0a9d0b7c7df074360f95df5270e6ec4060aca5de659718b7065e11","observation_id":"410f92a4-253b-4449-8d03-b5aba721a5ae","resolution":{"observed_at":"2026-08-02T08:21:33.457422Z","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":"2026-08-02T08:21:33.460845Z","title":"commfor-model-384","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.460845Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:cbd1346b7377699d91fe713ae0490454080e69b25ff81f996de3170b9539f60d","observation_id":"26b86651-828c-4849-a89d-15f2e0859626","resolution":{"observed_at":"2026-08-02T08:21:33.460845Z","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":"2026-08-02T08:21:33.463743Z","title":"Ntire2026_deepfake: Ntire 2026 challenge dinov3 ensemble","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.463743Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fab5267e560e6a7da5cf5ff8d366274534d45d2d8d698c935c202a6b72b66b62","observation_id":"2954951f-ba18-43d8-8217-c4db38dcaa6c","resolution":{"observed_at":"2026-08-02T08:21:33.463743Z","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":"2026-08-02T08:21:33.466636Z","title":"ai_vs_real_image_detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.466636Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:770cbac027ed1726b580943184c4451641f7dd1e644e9e3e5365d39276fa1bd1","observation_id":"a735ecaa-164b-479d-ae2b-6dfe0fcac5a7","resolution":{"observed_at":"2026-08-02T08:21:33.466636Z","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":"2026-08-02T08:21:33.469436Z","title":"ai-image-detector-siglip-dinov2","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.469436Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:c0cd031b546f7337a102f2390db1b40d8c3e5c8a03ea95fc8d4aaca2c34122c5","observation_id":"359aa758-b789-4f41-97c1-f6ebdd380f89","resolution":{"observed_at":"2026-08-02T08:21:33.469436Z","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":"2026-08-02T08:21:33.472053Z","title":"sdxl-detector","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.472053Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:2c95a571811b7d33ea5d8b764eeb12d176f5e9a50d64092a60870e2a29d03f9c","observation_id":"758a9be5-85cc-4540-b65d-fa08c4557504","resolution":{"observed_at":"2026-08-02T08:21:33.472053Z","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":"2026-08-02T08:21:33.474745Z","title":"Ai-vs-deepfake-vs-real-v2.0","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.474745Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fc8963b415df5e1eb57a1932e951f02799fd028d87189e7eab6c8f6af9dc6348","observation_id":"7f740f31-0052-41bc-b37a-12462053dcfb","resolution":{"observed_at":"2026-08-02T08:21:33.474745Z","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":"2026-08-02T08:21:33.477837Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.477837Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:f5b6519c395e9e2f5b36ace4219064cc3b519eb2a4683e1baa97c81516215a87","observation_id":"3e110218-cdf0-46c8-9180-85991b3fbe37","resolution":{"observed_at":"2026-08-02T08:21:33.477837Z","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":"2026-08-02T08:21:33.481095Z","title":"Ai-image-detector","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.481095Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:7e44cbdb54f644c01b813cb4de9692950bd1008b8120ef8ae5b3950015ed0219","observation_id":"d29b1044-d483-483a-a0a9-d912d0277ebd","resolution":{"observed_at":"2026-08-02T08:21:33.481095Z","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":"2026-08-02T08:21:33.484039Z","title":"ai-source-detector","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.484039Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:cf102709834faf72a2138403d64afe3660a209013f91be53083e1221dddcb7b4","observation_id":"13cfb442-05d0-41cb-8206-2f318f4e7687","resolution":{"observed_at":"2026-08-02T08:21:33.484039Z","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":"2026-08-02T08:21:33.486734Z","title":"flux-detector-vit","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.486734Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:906ea0df9bdeccbb3125f06360fed3fb0d09e1a3638da3965acac98f5e0aa10c","observation_id":"3ef75268-aed5-45c9-9ac7-5bebf00759cf","resolution":{"observed_at":"2026-08-02T08:21:33.486734Z","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":"2026-08-02T08:21:33.489510Z","title":"deepfake-model (deepguard)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.489510Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3d8e78c1640ce526928aacc7121a6c43af67b548770870be564050b2d4fa13b3","observation_id":"ab37709f-86a0-4ddd-a1b1-52ab9c2754c2","resolution":{"observed_at":"2026-08-02T08:21:33.489510Z","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":"2026-08-02T08:21:33.492348Z","title":"ai-image-detector-deploy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.492348Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:a9ea7145f477a00f831a73f83c20a8c9bd6e3fa75e815f72431ac702f4cc8a3a","observation_id":"b2e5d3fb-9f6e-4d34-8d38-ea6fe5540933","resolution":{"observed_at":"2026-08-02T08:21:33.492348Z","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":"2026-08-02T08:21:33.495148Z","title":"vit-real-fake-classification-v3","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.495148Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3cedb7d34006f777dc1adf50ef512caef7b2c755eafa1aa3e4d6593abb6b30d3","observation_id":"0601293b-28ad-4c9a-a743-8d493eb80422","resolution":{"observed_at":"2026-08-02T08:21:33.495148Z","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":"2026-08-02T08:21:33.497796Z","title":"ai-image-detect-distilled","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.497796Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:fa095c57322eba3a8c1d0520358ee6b75a47248ec64120964218149f75ca91d3","observation_id":"aa5e5c58-0b63-4c71-8770-a92c9f207d72","resolution":{"observed_at":"2026-08-02T08:21:33.497796Z","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":"2026-08-02T08:21:33.500407Z","title":"Opensight-communityforensics-deepfake-vit","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.500407Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:efd1b478c469359b7b0f9b399cdfd16bc03c3f0d9eb85c3e61afd5e1ae97f6cc","observation_id":"3517123f-37c2-4005-8ae0-21b719f5823f","resolution":{"observed_at":"2026-08-02T08:21:33.500407Z","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":"2026-08-02T08:21:33.503188Z","title":"ai-vs-human-image-detector","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:33.503188Z"},"links":{"citing_paper":"/paper/2607.06254"},"observation_digest":"sha256:3493c89ba1036055bd5ef35bf5a62f4ed3b9cfda760bb9204da11f5701fe6d66","observation_id":"051d6a37-f012-4854-a0d5-4e77a1bbd5dd","resolution":{"observed_at":"2026-08-02T08:21:33.503188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.06254","last_updated":"2026-07-29T09:32:00Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T08:21:31.533798Z","submitted_at":"2026-07-07T13:22:00Z","title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":100,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":107},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 100 of 107 outbound references and 0 inbound Pith citation observations for arXiv:2607.06254."}