{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2EZKLWZ4GCZJXVWZ7A67DZB5WP","short_pith_number":"pith:2EZKLWZ4","canonical_record":{"source":{"id":"2307.07036","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-13T19:27:40Z","cross_cats_sorted":[],"title_canon_sha256":"387ba168a82e5ed4ed8ebda04824e83876d4b8d607f39bb1f9a4fbea4def9fa3","abstract_canon_sha256":"800e12be9b76744c8eb869dd561063315f0dec61c81562fd9e3e27eb4a256ae3"},"schema_version":"1.0"},"canonical_sha256":"d132a5db3c30b29bd6d9f83df1e43db3dc015e9964f16458bc3fe5a02f5f4458","source":{"kind":"arxiv","id":"2307.07036","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.07036","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2307.07036v2","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.07036","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"2EZKLWZ4GCZJ","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"2EZKLWZ4GCZJXVWZ","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"2EZKLWZ4","created_at":"2026-07-05T10:23:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2EZKLWZ4GCZJXVWZ7A67DZB5WP","target":"record","payload":{"canonical_record":{"source":{"id":"2307.07036","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-13T19:27:40Z","cross_cats_sorted":[],"title_canon_sha256":"387ba168a82e5ed4ed8ebda04824e83876d4b8d607f39bb1f9a4fbea4def9fa3","abstract_canon_sha256":"800e12be9b76744c8eb869dd561063315f0dec61c81562fd9e3e27eb4a256ae3"},"schema_version":"1.0"},"canonical_sha256":"d132a5db3c30b29bd6d9f83df1e43db3dc015e9964f16458bc3fe5a02f5f4458","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:50.414179Z","signature_b64":"mDJtZhw6+dbKEuZTik7jhBzKZruePcCwYUqQkUHL7Wtp7DG3DOq8KA/q29t46Yxr+eFuCH7Wg1Zyvq2iurOoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d132a5db3c30b29bd6d9f83df1e43db3dc015e9964f16458bc3fe5a02f5f4458","last_reissued_at":"2026-07-05T10:23:50.413187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:50.413187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.07036","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"44XHjpqyI9QM0fq8EHGEWysA3HYTe80X1i+GlDFwzwhoRnXfgiDE7Dh5ZQ7Z/gIFwGqKPPipp/Pe8lF1wIGVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:30:59.474435Z"},"content_sha256":"9a3bacc6ada522d6f9f755cccc286b6b8b1f781e1833c80479a802e74917f68b","schema_version":"1.0","event_id":"sha256:9a3bacc6ada522d6f9f755cccc286b6b8b1f781e1833c80479a802e74917f68b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2EZKLWZ4GCZJXVWZ7A67DZB5WP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deressa Wodajo Deressa, Glenn Van Wallendael, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar","submitted_at":"2023-07-13T19:27:40Z","abstract_excerpt":"Deepfakes have raised significant concerns due to their potential to spread false information and compromise digital media integrity. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes Autoencoder and Variational Autoencoder to learn from the latent data distribution. By learning from the visual artifa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.07036","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/2307.07036/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":"2026-07-05T10:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FhIc8z+/n4xEXnjpMHR19u/x3tvtx8LsmNyaQwDY/EifddyMZf1w5bv5iYlpjQk+GvjTCoUbvvJ77zwgKAitCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:30:59.475149Z"},"content_sha256":"0f4843c1a987965add8780e338c5071a8833ce2b549bd1a8de39673e678fab38","schema_version":"1.0","event_id":"sha256:0f4843c1a987965add8780e338c5071a8833ce2b549bd1a8de39673e678fab38"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/bundle.json","state_url":"https://pith.science/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/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":"2026-08-10T06:30:59Z","links":{"resolver":"https://pith.science/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP","bundle":"https://pith.science/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/bundle.json","state":"https://pith.science/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2EZKLWZ4GCZJXVWZ7A67DZB5WP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2EZKLWZ4GCZJXVWZ7A67DZB5WP","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":"800e12be9b76744c8eb869dd561063315f0dec61c81562fd9e3e27eb4a256ae3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-13T19:27:40Z","title_canon_sha256":"387ba168a82e5ed4ed8ebda04824e83876d4b8d607f39bb1f9a4fbea4def9fa3"},"schema_version":"1.0","source":{"id":"2307.07036","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.07036","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2307.07036v2","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.07036","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"2EZKLWZ4GCZJ","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"2EZKLWZ4GCZJXVWZ","created_at":"2026-07-05T10:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"2EZKLWZ4","created_at":"2026-07-05T10:23:50Z"}],"graph_snapshots":[{"event_id":"sha256:0f4843c1a987965add8780e338c5071a8833ce2b549bd1a8de39673e678fab38","target":"graph","created_at":"2026-07-05T10:23:50Z","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/2307.07036/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deepfakes have raised significant concerns due to their potential to spread false information and compromise digital media integrity. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes Autoencoder and Variational Autoencoder to learn from the latent data distribution. By learning from the visual artifa","authors_text":"Deressa Wodajo Deressa, Glenn Van Wallendael, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-13T19:27:40Z","title":"GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.07036","kind":"arxiv","version":2},"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":"sha256:9a3bacc6ada522d6f9f755cccc286b6b8b1f781e1833c80479a802e74917f68b","target":"record","created_at":"2026-07-05T10:23:50Z","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":"800e12be9b76744c8eb869dd561063315f0dec61c81562fd9e3e27eb4a256ae3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-13T19:27:40Z","title_canon_sha256":"387ba168a82e5ed4ed8ebda04824e83876d4b8d607f39bb1f9a4fbea4def9fa3"},"schema_version":"1.0","source":{"id":"2307.07036","kind":"arxiv","version":2}},"canonical_sha256":"d132a5db3c30b29bd6d9f83df1e43db3dc015e9964f16458bc3fe5a02f5f4458","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d132a5db3c30b29bd6d9f83df1e43db3dc015e9964f16458bc3fe5a02f5f4458","first_computed_at":"2026-07-05T10:23:50.413187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:23:50.413187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mDJtZhw6+dbKEuZTik7jhBzKZruePcCwYUqQkUHL7Wtp7DG3DOq8KA/q29t46Yxr+eFuCH7Wg1Zyvq2iurOoBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:23:50.414179Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.07036","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a3bacc6ada522d6f9f755cccc286b6b8b1f781e1833c80479a802e74917f68b","sha256:0f4843c1a987965add8780e338c5071a8833ce2b549bd1a8de39673e678fab38"],"state_sha256":"62c37d29bdd61ca5cb9e51c9968a6198728bf3c1b89aabf0d75db58d5d16590c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MNxeHj5t/y7VNbXwjjZYlqbWW5vpYW8OMdkDuKHtT7Lp+zsV2y5bzlSZWps+nUICNShri1AVpoq7XsM+pBUnDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:30:59.480988Z","bundle_sha256":"2595257a49d2bb93ac58d70663403f3649175e5bc2e43ffecc4eb9bf64bbbd54"}}