{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AYZ4YC25QS7QT57IC237TTNN7Q","short_pith_number":"pith:AYZ4YC25","schema_version":"1.0","canonical_sha256":"0633cc0b5d84bf09f7e816b7f9cdadfc1e18986438e30509b0a6e59318180e18","source":{"kind":"arxiv","id":"2607.03562","version":1},"attestation_state":"computed","paper":{"title":"XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhijeet Narang, Abhinav Dhall, Jianfei Cai, Kartik Kuckreja, Muhammad Haris Khan, Shreya Ghosh","submitted_at":"2026-07-03T18:52:42Z","abstract_excerpt":"As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user trust. Existing benchmarks mainly evaluate classification accuracy, overlooking whether explanations reflect the actual manipulations. This gap hinders progress toward deployable, explainable deepfake detection systems. To this end, we introduce XPlainVerse, a large-scale benchmark designed for joint deepfake detection and human-centered explanation. XPlainVerse comprises one million real and manipulated images, pairin"},"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":"2607.03562","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-03T18:52:42Z","cross_cats_sorted":[],"title_canon_sha256":"8a16c4791485906cd6fcf9009c113ecb45b583d47323233488e31e74c18542e0","abstract_canon_sha256":"12bf471c15bf315f9514abb9a528202b97a561f3901fcfbde787d6dd7daa1e29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:17:54.671777Z","signature_b64":"KK9HFu5MqqufAxYtOrF691s9GIy8a7aMhXwRuU8/uyFGqZYoStvgnGwoC6BZoggDHe6w5aRkMktTcdC4lM8uCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0633cc0b5d84bf09f7e816b7f9cdadfc1e18986438e30509b0a6e59318180e18","last_reissued_at":"2026-07-07T02:17:54.670956Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:17:54.670956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhijeet Narang, Abhinav Dhall, Jianfei Cai, Kartik Kuckreja, Muhammad Haris Khan, Shreya Ghosh","submitted_at":"2026-07-03T18:52:42Z","abstract_excerpt":"As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user trust. Existing benchmarks mainly evaluate classification accuracy, overlooking whether explanations reflect the actual manipulations. This gap hinders progress toward deployable, explainable deepfake detection systems. To this end, we introduce XPlainVerse, a large-scale benchmark designed for joint deepfake detection and human-centered explanation. XPlainVerse comprises one million real and manipulated images, pairin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.03562","kind":"arxiv","version":1},"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/2607.03562/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":"2607.03562","created_at":"2026-07-07T02:17:54.671082+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.03562v1","created_at":"2026-07-07T02:17:54.671082+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.03562","created_at":"2026-07-07T02:17:54.671082+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYZ4YC25QS7Q","created_at":"2026-07-07T02:17:54.671082+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYZ4YC25QS7QT57I","created_at":"2026-07-07T02:17:54.671082+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYZ4YC25","created_at":"2026-07-07T02:17:54.671082+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q","json":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q.json","graph_json":"https://pith.science/api/pith-number/AYZ4YC25QS7QT57IC237TTNN7Q/graph.json","events_json":"https://pith.science/api/pith-number/AYZ4YC25QS7QT57IC237TTNN7Q/events.json","paper":"https://pith.science/paper/AYZ4YC25"},"agent_actions":{"view_html":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q","download_json":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q.json","view_paper":"https://pith.science/paper/AYZ4YC25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.03562&json=true","fetch_graph":"https://pith.science/api/pith-number/AYZ4YC25QS7QT57IC237TTNN7Q/graph.json","fetch_events":"https://pith.science/api/pith-number/AYZ4YC25QS7QT57IC237TTNN7Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q/action/storage_attestation","attest_author":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q/action/author_attestation","sign_citation":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q/action/citation_signature","submit_replication":"https://pith.science/pith/AYZ4YC25QS7QT57IC237TTNN7Q/action/replication_record"}},"created_at":"2026-07-07T02:17:54.671082+00:00","updated_at":"2026-07-07T02:17:54.671082+00:00"}