{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:NX7GUKEUADWLZFWRCDSUR2BHN4","short_pith_number":"pith:NX7GUKEU","schema_version":"1.0","canonical_sha256":"6dfe6a289400ecbc96d110e548e8276f1369a9961f4f66af11c92934f7f8a5f0","source":{"kind":"arxiv","id":"2001.03024","version":2},"attestation_state":"computed","paper":{"title":"DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Chen Qian, Liming Jiang, Ren Li, Wayne Wu","submitted_at":"2020-01-09T14:37:17Z","abstract_excerpt":"We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics-1.0 are carefully collected, and fake videos are generated by a newly proposed end-to-end face sw"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2001.03024","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-09T14:37:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"88bc161384f18592d30b4f9ca0b304e8d6586a2af9deb841e5100b1aa0c85875","abstract_canon_sha256":"85a24d84a0e17e8f7a4e02e551d7bc8b2f61ee4554dbc3f3bbc8750a08af5096"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:58:39.968164Z","signature_b64":"UM5N897HvxTjwY6igeectNJIsvmtB9+OSY1eC4+OYiaE6s0ZNNhW6Mh/qct3FUB3b+g887Oq4FxsR64WFj6eBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dfe6a289400ecbc96d110e548e8276f1369a9961f4f66af11c92934f7f8a5f0","last_reissued_at":"2026-07-05T01:58:39.967772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:58:39.967772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Chen Qian, Liming Jiang, Ren Li, Wayne Wu","submitted_at":"2020-01-09T14:37:17Z","abstract_excerpt":"We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics-1.0 are carefully collected, and fake videos are generated by a newly proposed end-to-end face sw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.03024","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.03024/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2001.03024","created_at":"2026-07-05T01:58:39.967828+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.03024v2","created_at":"2026-07-05T01:58:39.967828+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.03024","created_at":"2026-07-05T01:58:39.967828+00:00"},{"alias_kind":"pith_short_12","alias_value":"NX7GUKEUADWL","created_at":"2026-07-05T01:58:39.967828+00:00"},{"alias_kind":"pith_short_16","alias_value":"NX7GUKEUADWLZFWR","created_at":"2026-07-05T01:58:39.967828+00:00"},{"alias_kind":"pith_short_8","alias_value":"NX7GUKEU","created_at":"2026-07-05T01:58:39.967828+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07398","citing_title":"Exposing and Mitigating Temporal Attack in Deepfake Video Detection","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4","json":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4.json","graph_json":"https://pith.science/api/pith-number/NX7GUKEUADWLZFWRCDSUR2BHN4/graph.json","events_json":"https://pith.science/api/pith-number/NX7GUKEUADWLZFWRCDSUR2BHN4/events.json","paper":"https://pith.science/paper/NX7GUKEU"},"agent_actions":{"view_html":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4","download_json":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4.json","view_paper":"https://pith.science/paper/NX7GUKEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.03024&json=true","fetch_graph":"https://pith.science/api/pith-number/NX7GUKEUADWLZFWRCDSUR2BHN4/graph.json","fetch_events":"https://pith.science/api/pith-number/NX7GUKEUADWLZFWRCDSUR2BHN4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4/action/storage_attestation","attest_author":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4/action/author_attestation","sign_citation":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4/action/citation_signature","submit_replication":"https://pith.science/pith/NX7GUKEUADWLZFWRCDSUR2BHN4/action/replication_record"}},"created_at":"2026-07-05T01:58:39.967828+00:00","updated_at":"2026-07-05T01:58:39.967828+00:00"}