{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:W2NLPCSE2JQUQYZF76WMZLYSPQ","short_pith_number":"pith:W2NLPCSE","schema_version":"1.0","canonical_sha256":"b69ab78a44d261486325ffacccaf127c177f6c7bc07779910ed638f170712c30","source":{"kind":"arxiv","id":"2103.00484","version":2},"attestation_state":"computed","paper":{"title":"Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS","eess.IV"],"primary_cat":"cs.CR","authors_text":"Ali Javed, Aun Irtaza, Khalid Mahmood Malik, Marriam Nawaz, Momina Masood","submitted_at":"2021-02-25T18:26:50Z","abstract_excerpt":"Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, open-source trained models, and economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods, especially Generative Adversarial Networks (GAN), have made it possible to generate deepfakes to disseminate disinformation, revenge porn, financial frauds, hoaxes, and to disrupt government functioning. The existing surveys have mainly focused on the detection of deepfake images and videos. This paper provides a comprehensive review and detaile"},"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":"2103.00484","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2021-02-25T18:26:50Z","cross_cats_sorted":["cs.LG","cs.SD","eess.AS","eess.IV"],"title_canon_sha256":"23487056c2cbe01351a9293ffc7a2c7d938132ac066c64a07f124d2e9a37c829","abstract_canon_sha256":"4983264e1913d8d543ac10e655bff055fd54b7f023ad0e60733bc411037f9974"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:05.763557Z","signature_b64":"UZmu44MGgwXpgTf9LioJ2n1bBkLsKqdEdEVHDo6zr6wE1hRZl0nxXT8bBn9KOUvmXk6P69qX/t3cIxMIR8tDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b69ab78a44d261486325ffacccaf127c177f6c7bc07779910ed638f170712c30","last_reissued_at":"2026-07-05T03:34:05.763076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:05.763076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS","eess.IV"],"primary_cat":"cs.CR","authors_text":"Ali Javed, Aun Irtaza, Khalid Mahmood Malik, Marriam Nawaz, Momina Masood","submitted_at":"2021-02-25T18:26:50Z","abstract_excerpt":"Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, open-source trained models, and economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods, especially Generative Adversarial Networks (GAN), have made it possible to generate deepfakes to disseminate disinformation, revenge porn, financial frauds, hoaxes, and to disrupt government functioning. The existing surveys have mainly focused on the detection of deepfake images and videos. This paper provides a comprehensive review and detaile"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.00484","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/2103.00484/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":"2103.00484","created_at":"2026-07-05T03:34:05.763130+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.00484v2","created_at":"2026-07-05T03:34:05.763130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.00484","created_at":"2026-07-05T03:34:05.763130+00:00"},{"alias_kind":"pith_short_12","alias_value":"W2NLPCSE2JQU","created_at":"2026-07-05T03:34:05.763130+00:00"},{"alias_kind":"pith_short_16","alias_value":"W2NLPCSE2JQUQYZF","created_at":"2026-07-05T03:34:05.763130+00:00"},{"alias_kind":"pith_short_8","alias_value":"W2NLPCSE","created_at":"2026-07-05T03:34:05.763130+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11927","citing_title":"A Lightweight and Interpretable Deepfakes Detection Framework","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ","json":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ.json","graph_json":"https://pith.science/api/pith-number/W2NLPCSE2JQUQYZF76WMZLYSPQ/graph.json","events_json":"https://pith.science/api/pith-number/W2NLPCSE2JQUQYZF76WMZLYSPQ/events.json","paper":"https://pith.science/paper/W2NLPCSE"},"agent_actions":{"view_html":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ","download_json":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ.json","view_paper":"https://pith.science/paper/W2NLPCSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.00484&json=true","fetch_graph":"https://pith.science/api/pith-number/W2NLPCSE2JQUQYZF76WMZLYSPQ/graph.json","fetch_events":"https://pith.science/api/pith-number/W2NLPCSE2JQUQYZF76WMZLYSPQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ/action/storage_attestation","attest_author":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ/action/author_attestation","sign_citation":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ/action/citation_signature","submit_replication":"https://pith.science/pith/W2NLPCSE2JQUQYZF76WMZLYSPQ/action/replication_record"}},"created_at":"2026-07-05T03:34:05.763130+00:00","updated_at":"2026-07-05T03:34:05.763130+00:00"}