{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OV5MD73DWHZ4DLRFDBPATUR765","short_pith_number":"pith:OV5MD73D","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"},"canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","source":{"kind":"arxiv","id":"2605.17573","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17573","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17573v1","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17573","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"OV5MD73DWHZ4","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_16","alias_value":"OV5MD73DWHZ4DLRF","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_8","alias_value":"OV5MD73D","created_at":"2026-05-20T00:04:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OV5MD73DWHZ4DLRFDBPATUR765","target":"record","payload":{"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"},"canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","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"},"source_kind":"arxiv","source_id":"2605.17573","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fMBNJO9HWw+lJmQyMiOlLgL9KDnDuKUZ4F6fqRdensoLyZb0veqsSOpsTOwU0Kxj66s1zmoUbFJQJZL+nuxrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T09:11:59.466802Z"},"content_sha256":"7ce217f681d6fef4221faea616bfd19833ad58366917bbe4c7535ee59bdf778f","schema_version":"1.0","event_id":"sha256:7ce217f681d6fef4221faea616bfd19833ad58366917bbe4c7535ee59bdf778f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OV5MD73DWHZ4DLRFDBPATUR765","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OotBhySfpSa1Nsir3Dyo0tq7Wqq593HXYLUxViuO++BT9U/kq+EK6vxlv7/g559V3gZa7rKGpSwh7rTESgxnDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T09:11:59.467214Z"},"content_sha256":"b1cfad314e8621496f32a1e4ec025de9a45b08ea351850bcbbab7e061e1e4e37","schema_version":"1.0","event_id":"sha256:b1cfad314e8621496f32a1e4ec025de9a45b08ea351850bcbbab7e061e1e4e37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/bundle.json","state_url":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OV5MD73DWHZ4DLRFDBPATUR765/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T09:11:59Z","links":{"resolver":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765","bundle":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/bundle.json","state":"https://pith.science/pith/OV5MD73DWHZ4DLRFDBPATUR765/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OV5MD73DWHZ4DLRFDBPATUR765/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OV5MD73DWHZ4DLRFDBPATUR765","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"35b46d8521c59c3bcde2c5e0dfc8fd6fe089c43997738c8b123947fcce59f993","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:01:32Z","title_canon_sha256":"1067e5e623740c10f3f608d8b12c3cbdb36ca01b4f43fce0c870e259e5851ee6"},"schema_version":"1.0","source":{"id":"2605.17573","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17573","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17573v1","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17573","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"OV5MD73DWHZ4","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_16","alias_value":"OV5MD73DWHZ4DLRF","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_8","alias_value":"OV5MD73D","created_at":"2026-05-20T00:04:46Z"}],"graph_snapshots":[{"event_id":"sha256:b1cfad314e8621496f32a1e4ec025de9a45b08ea351850bcbbab7e061e1e4e37","target":"graph","created_at":"2026-05-20T00:04:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"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"}],"endpoint":"/pith/2605.17573/integrity.json","findings":[],"snapshot_sha256":"86837a3ad4b4a581734febdf97f59585015f91f78dd7094bce978d3cd36fb3a0","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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 ","authors_text":"Mohammadreza Rashidi, Raja Hashim Ali, Sami Ur Rahman","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:01:32Z","title":"Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17573","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7ce217f681d6fef4221faea616bfd19833ad58366917bbe4c7535ee59bdf778f","target":"record","created_at":"2026-05-20T00:04:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"35b46d8521c59c3bcde2c5e0dfc8fd6fe089c43997738c8b123947fcce59f993","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:01:32Z","title_canon_sha256":"1067e5e623740c10f3f608d8b12c3cbdb36ca01b4f43fce0c870e259e5851ee6"},"schema_version":"1.0","source":{"id":"2605.17573","kind":"arxiv","version":1}},"canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"757ac1ff63b1f3c1ae25185e09d23ff766eecf8005b711f7e7fc9d3b3fbd8028","first_computed_at":"2026-05-20T00:04:46.630504Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:46.630504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kxpYeJLD0lDySHcRql5XCGuhoMcCVkpHvlOF6ujGpAC4MfYbIOhQ9/Bpas8dQN13r3Jwakxy5wQf48R8hNgkAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:46.631404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17573","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ce217f681d6fef4221faea616bfd19833ad58366917bbe4c7535ee59bdf778f","sha256:b1cfad314e8621496f32a1e4ec025de9a45b08ea351850bcbbab7e061e1e4e37"],"state_sha256":"40fd902d8f3d35904b9899ad4f9efd2e9df2aab47aa50a7c2ee7b5a4d0eaa70f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bD6XJZkgDkTcO7gqVnXSvfAjgsx0EyOxabqdHAd+XvKDuUkQPGE2BgX2F5qbPnArhztt3cTQlf4xnr+LAzwUBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T09:11:59.469400Z","bundle_sha256":"0cbaadafebfd94ca4d135f8d85b360e05e9f714498a86d426354e51213736647"}}