{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ","short_pith_number":"pith:IJ7QQ4IP","schema_version":"1.0","canonical_sha256":"427f08710f82321c00fe833d1e2b3a7c105b5a937515cf69d3ad265137989954","source":{"kind":"arxiv","id":"2102.11126","version":3},"attestation_state":"computed","paper":{"title":"Deepfake Video Detection Using Convolutional Vision Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deressa Wodajo, Solomon Atnafu","submitted_at":"2021-02-22T15:56:05Z","abstract_excerpt":"The rapid advancement of deep learning models that can generate and synthesis hyper-realistic videos known as Deepfakes and their ease of access to the general public have raised concern from all concerned bodies to their possible malicious intent use. Deep learning techniques can now generate faces, swap faces between two subjects in a video, alter facial expressions, change gender, and alter facial features, to list a few. These powerful video manipulation methods have potential use in many fields. However, they also pose a looming threat to everyone if used for harmful purposes such as iden"},"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":"2102.11126","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-02-22T15:56:05Z","cross_cats_sorted":[],"title_canon_sha256":"d4177792b353909a9adf607777d37ad3235251bf287c0a29ab4a87e0bba592b9","abstract_canon_sha256":"e9f2de7b76eaad800830b850520faf9e7936fd18560d5addf9c975b30ffbc763"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:22:17.810195Z","signature_b64":"h1msMW8/hjrdVez+GpO+UT+uLRXOOA/BGWx1mvhQpzFbPP6W1dJRNkDzKxRuXFNbtRo86lmgbH+tEJQWxDjTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427f08710f82321c00fe833d1e2b3a7c105b5a937515cf69d3ad265137989954","last_reissued_at":"2026-07-05T02:22:17.809668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:22:17.809668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deepfake Video Detection Using Convolutional Vision Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deressa Wodajo, Solomon Atnafu","submitted_at":"2021-02-22T15:56:05Z","abstract_excerpt":"The rapid advancement of deep learning models that can generate and synthesis hyper-realistic videos known as Deepfakes and their ease of access to the general public have raised concern from all concerned bodies to their possible malicious intent use. Deep learning techniques can now generate faces, swap faces between two subjects in a video, alter facial expressions, change gender, and alter facial features, to list a few. These powerful video manipulation methods have potential use in many fields. However, they also pose a looming threat to everyone if used for harmful purposes such as iden"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.11126","kind":"arxiv","version":3},"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/2102.11126/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":"2102.11126","created_at":"2026-07-05T02:22:17.809729+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.11126v3","created_at":"2026-07-05T02:22:17.809729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.11126","created_at":"2026-07-05T02:22:17.809729+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ7QQ4IPQIZB","created_at":"2026-07-05T02:22:17.809729+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ7QQ4IPQIZBYAH6","created_at":"2026-07-05T02:22:17.809729+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ7QQ4IP","created_at":"2026-07-05T02:22:17.809729+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06666","citing_title":"Architecture-Adaptive Uncertainty Fusion for Deepfake Detection","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2504.14129","citing_title":"PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19630","citing_title":"EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04086","citing_title":"LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03390","citing_title":"Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10071","citing_title":"MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12650","citing_title":"Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ","json":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ.json","graph_json":"https://pith.science/api/pith-number/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/graph.json","events_json":"https://pith.science/api/pith-number/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/events.json","paper":"https://pith.science/paper/IJ7QQ4IP"},"agent_actions":{"view_html":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ","download_json":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ.json","view_paper":"https://pith.science/paper/IJ7QQ4IP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.11126&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/action/storage_attestation","attest_author":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/action/author_attestation","sign_citation":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/action/citation_signature","submit_replication":"https://pith.science/pith/IJ7QQ4IPQIZBYAH6QM6R4KZ2PQ/action/replication_record"}},"created_at":"2026-07-05T02:22:17.809729+00:00","updated_at":"2026-07-05T02:22:17.809729+00:00"}