{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7MX4G4KPXBOJMBZQOSPDD7YMWY","short_pith_number":"pith:7MX4G4KP","canonical_record":{"source":{"id":"2108.12715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-28T22:56:09Z","cross_cats_sorted":[],"title_canon_sha256":"519044a79af5544da16cc54985e3cb112cfe9e9e8a5979148f76cee78ecbfc61","abstract_canon_sha256":"01e002e9a57dc46e95dabdaae94324d1f16ad14b8e33d6069dd7b9983586aff4"},"schema_version":"1.0"},"canonical_sha256":"fb2fc3714fb85c960730749e31ff0cb6324ccd2726a244744d5c58bbd8f82f84","source":{"kind":"arxiv","id":"2108.12715","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.12715","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"arxiv_version","alias_value":"2108.12715v1","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.12715","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_12","alias_value":"7MX4G4KPXBOJ","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_16","alias_value":"7MX4G4KPXBOJMBZQ","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_8","alias_value":"7MX4G4KP","created_at":"2026-07-05T03:09:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7MX4G4KPXBOJMBZQOSPDD7YMWY","target":"record","payload":{"canonical_record":{"source":{"id":"2108.12715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-28T22:56:09Z","cross_cats_sorted":[],"title_canon_sha256":"519044a79af5544da16cc54985e3cb112cfe9e9e8a5979148f76cee78ecbfc61","abstract_canon_sha256":"01e002e9a57dc46e95dabdaae94324d1f16ad14b8e33d6069dd7b9983586aff4"},"schema_version":"1.0"},"canonical_sha256":"fb2fc3714fb85c960730749e31ff0cb6324ccd2726a244744d5c58bbd8f82f84","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:09:34.848249Z","signature_b64":"luM91TjbDP4DAvBonjZEs5JmPn6Dky9vY0ikXijV53/Sk6D4U7nC8Ahf4OqjX0pcQe1CvgO/64MWD0vgy+DQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb2fc3714fb85c960730749e31ff0cb6324ccd2726a244744d5c58bbd8f82f84","last_reissued_at":"2026-07-05T03:09:34.847858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:09:34.847858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.12715","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":"2026-07-05T03:09:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1STWiuoVgi4S8nsgcOVmXqE9T4EVWr3DHLwiyC2KOfsFRfSN/5WRaO40xKrftn9MpW55fjVtL48PlPr5cc1aBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:22:40.304949Z"},"content_sha256":"0af888e6793b00232b0012de47320347241fdfe934490e5a5f4bd1f61c56b10d","schema_version":"1.0","event_id":"sha256:0af888e6793b00232b0012de47320347241fdfe934490e5a5f4bd1f61c56b10d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7MX4G4KPXBOJMBZQOSPDD7YMWY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kevin Lutz, Robert Bassett","submitted_at":"2021-08-28T22:56:09Z","abstract_excerpt":"Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area. In this paper, we analyze an existing DeepFake detection technique based on head pose estimation, which can be applied when fake images are generated with an autoencoder-based face swap. Existing literature suggests that this method is an effective DeepFake detector, and its motivating principles are attractively simple. With an eye towards using these principles to develop new Dee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.12715","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/2108.12715/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:09:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AqYN4bIS49051ZYotdl6tw6HwG1T5SH53NgC2uRTME4Wmyuq/YK/4oARihgL/0tOS1VC1S1l4oEwpaeL7pOZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:22:40.305323Z"},"content_sha256":"6366f048052ea7f5fc526038e471d031cfa6741cda7941fde808ca6d8b6b57e1","schema_version":"1.0","event_id":"sha256:6366f048052ea7f5fc526038e471d031cfa6741cda7941fde808ca6d8b6b57e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/bundle.json","state_url":"https://pith.science/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/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":"2026-08-18T13:22:40Z","links":{"resolver":"https://pith.science/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY","bundle":"https://pith.science/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/bundle.json","state":"https://pith.science/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7MX4G4KPXBOJMBZQOSPDD7YMWY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7MX4G4KPXBOJMBZQOSPDD7YMWY","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":"01e002e9a57dc46e95dabdaae94324d1f16ad14b8e33d6069dd7b9983586aff4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-28T22:56:09Z","title_canon_sha256":"519044a79af5544da16cc54985e3cb112cfe9e9e8a5979148f76cee78ecbfc61"},"schema_version":"1.0","source":{"id":"2108.12715","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.12715","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"arxiv_version","alias_value":"2108.12715v1","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.12715","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_12","alias_value":"7MX4G4KPXBOJ","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_16","alias_value":"7MX4G4KPXBOJMBZQ","created_at":"2026-07-05T03:09:34Z"},{"alias_kind":"pith_short_8","alias_value":"7MX4G4KP","created_at":"2026-07-05T03:09:34Z"}],"graph_snapshots":[{"event_id":"sha256:6366f048052ea7f5fc526038e471d031cfa6741cda7941fde808ca6d8b6b57e1","target":"graph","created_at":"2026-07-05T03:09:34Z","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":[],"endpoint":"/pith/2108.12715/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area. In this paper, we analyze an existing DeepFake detection technique based on head pose estimation, which can be applied when fake images are generated with an autoencoder-based face swap. Existing literature suggests that this method is an effective DeepFake detector, and its motivating principles are attractively simple. With an eye towards using these principles to develop new Dee","authors_text":"Kevin Lutz, Robert Bassett","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-28T22:56:09Z","title":"DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.12715","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":"sha256:0af888e6793b00232b0012de47320347241fdfe934490e5a5f4bd1f61c56b10d","target":"record","created_at":"2026-07-05T03:09:34Z","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":"01e002e9a57dc46e95dabdaae94324d1f16ad14b8e33d6069dd7b9983586aff4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-28T22:56:09Z","title_canon_sha256":"519044a79af5544da16cc54985e3cb112cfe9e9e8a5979148f76cee78ecbfc61"},"schema_version":"1.0","source":{"id":"2108.12715","kind":"arxiv","version":1}},"canonical_sha256":"fb2fc3714fb85c960730749e31ff0cb6324ccd2726a244744d5c58bbd8f82f84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb2fc3714fb85c960730749e31ff0cb6324ccd2726a244744d5c58bbd8f82f84","first_computed_at":"2026-07-05T03:09:34.847858Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:09:34.847858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"luM91TjbDP4DAvBonjZEs5JmPn6Dky9vY0ikXijV53/Sk6D4U7nC8Ahf4OqjX0pcQe1CvgO/64MWD0vgy+DQBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:09:34.848249Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.12715","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0af888e6793b00232b0012de47320347241fdfe934490e5a5f4bd1f61c56b10d","sha256:6366f048052ea7f5fc526038e471d031cfa6741cda7941fde808ca6d8b6b57e1"],"state_sha256":"cc2c5dda74b13e4d7910af7290c4e2e84259f3a921dc9564439dadcb670aaffb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j61PBTgMDmZ3tDgYdBDNePzZq73PL4kwsAbgXZKWYJaxyRL8nlFipV1bJIxAKzRBVirTx8cWmw7+b5GPVoVwCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T13:22:40.307643Z","bundle_sha256":"38cb91d281fa8db40ce4acdefd82ae7de7b6957eec006e614e8fd9bdf734df88"}}