{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OV5MD73DWHZ4DLRFDBPATUR765","short_pith_number":"pith:OV5MD73D","schema_version":"1.0","canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","source":{"kind":"arxiv","id":"2605.17573","version":1},"attestation_state":"computed","paper":{"title":"Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Mohammadreza Rashidi, Raja Hashim Ali, Sami Ur Rahman","submitted_at":"2026-05-17T18:01:32Z","abstract_excerpt":"Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identity-based attacks. Frame-level deepfake detectors degrade sharply as generator quality increases; high-quality 128x128 GAN output cuts spatial-only accuracy by five percentage points while leaving temporal inconsistencies largely intact. We address this gap with a 3D Convolutional Neural Network detector based on R3D-18, trained with a composite loss that combines binary cross-entropy with a temporal-consistency regularizer. The model processes "},"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":"2605.17573","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:01:32Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1067e5e623740c10f3f608d8b12c3cbdb36ca01b4f43fce0c870e259e5851ee6","abstract_canon_sha256":"35b46d8521c59c3bcde2c5e0dfc8fd6fe089c43997738c8b123947fcce59f993"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:46.631404Z","signature_b64":"kxpYeJLD0lDySHcRql5XCGuhoMcCVkpHvlOF6ujGpAC4MfYbIOhQ9/Bpas8dQN13r3Jwakxy5wQf48R8hNgkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","last_reissued_at":"2026-05-20T00:04:46.630504Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:46.630504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Mohammadreza Rashidi, Raja Hashim Ali, Sami Ur Rahman","submitted_at":"2026-05-17T18:01:32Z","abstract_excerpt":"Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identity-based attacks. Frame-level deepfake detectors degrade sharply as generator quality increases; high-quality 128x128 GAN output cuts spatial-only accuracy by five percentage points while leaving temporal inconsistencies largely intact. We address this gap with a 3D Convolutional Neural Network detector based on R3D-18, trained with a composite loss that combines binary cross-entropy with a temporal-consistency regularizer. The model processes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17573","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/2605.17573/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.593412Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.524634Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"86837a3ad4b4a581734febdf97f59585015f91f78dd7094bce978d3cd36fb3a0"},"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":"2605.17573","created_at":"2026-05-20T00:04:46.630646+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17573v1","created_at":"2026-05-20T00:04:46.630646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17573","created_at":"2026-05-20T00:04:46.630646+00:00"},{"alias_kind":"pith_short_12","alias_value":"OV5MD73DWHZ4","created_at":"2026-05-20T00:04:46.630646+00:00"},{"alias_kind":"pith_short_16","alias_value":"OV5MD73DWHZ4DLRF","created_at":"2026-05-20T00:04:46.630646+00:00"},{"alias_kind":"pith_short_8","alias_value":"OV5MD73D","created_at":"2026-05-20T00:04:46.630646+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/OV5MD73DWHZ4DLRFDBPATUR765","json":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765.json","graph_json":"https://pith.science/api/pith-number/OV5MD73DWHZ4DLRFDBPATUR765/graph.json","events_json":"https://pith.science/api/pith-number/OV5MD73DWHZ4DLRFDBPATUR765/events.json","paper":"https://pith.science/paper/OV5MD73D"},"agent_actions":{"view_html":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765","download_json":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765.json","view_paper":"https://pith.science/paper/OV5MD73D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17573&json=true","fetch_graph":"https://pith.science/api/pith-number/OV5MD73DWHZ4DLRFDBPATUR765/graph.json","fetch_events":"https://pith.science/api/pith-number/OV5MD73DWHZ4DLRFDBPATUR765/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/action/storage_attestation","attest_author":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/action/author_attestation","sign_citation":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/action/citation_signature","submit_replication":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/action/replication_record"}},"created_at":"2026-05-20T00:04:46.630646+00:00","updated_at":"2026-05-20T00:04:46.630646+00:00"}