{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:5IKURODK4KU5OAEQQOIXH4X6Y7","short_pith_number":"pith:5IKURODK","schema_version":"1.0","canonical_sha256":"ea1548b86ae2a9d70090839173f2fec7f91c8161f3399c578a208931474445b0","source":{"kind":"arxiv","id":"2110.00740","version":1},"attestation_state":"computed","paper":{"title":"FICGAN: Facial Identity Controllable GAN for De-identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Doyeon Kim, Heonseok Ha, Jooyoung Choi, Sungroh Yoon, Sungwon Kim, Tae-Hyun Oh, Yonghyun Jeong, Youngmin Ro","submitted_at":"2021-10-02T07:09:27Z","abstract_excerpt":"In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility. We tackle the less-explored yet desired functionality in face de-identification based on the two factors. First, we focus on the challenging issue to obtain a high level of privacy protection in the de-identification task while uncompromising the image quality. Second, we analyze the facial attributes related to identity and non-identity and explor"},"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":"2110.00740","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-02T07:09:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"73ebcb8b3187c9c15497863c4b5cdbde68f00569d1ad345e63bb4799d7630530","abstract_canon_sha256":"6418bf84bf09491be2bbde6ece77b754aed8341c29a6961fbe9eaff2e1ffdcea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:19:47.942051Z","signature_b64":"d/IqRViaZ1HrffbLTxJ8nW0baxdEkKFjmES4NpidJE4QQE+HXN7/8MEnEU4/2kVOgsup3KevWQ3fFiKdvEP6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea1548b86ae2a9d70090839173f2fec7f91c8161f3399c578a208931474445b0","last_reissued_at":"2026-07-05T03:19:47.941594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:19:47.941594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FICGAN: Facial Identity Controllable GAN for De-identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Doyeon Kim, Heonseok Ha, Jooyoung Choi, Sungroh Yoon, Sungwon Kim, Tae-Hyun Oh, Yonghyun Jeong, Youngmin Ro","submitted_at":"2021-10-02T07:09:27Z","abstract_excerpt":"In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility. We tackle the less-explored yet desired functionality in face de-identification based on the two factors. First, we focus on the challenging issue to obtain a high level of privacy protection in the de-identification task while uncompromising the image quality. Second, we analyze the facial attributes related to identity and non-identity and explor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.00740","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/2110.00740/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":"2110.00740","created_at":"2026-07-05T03:19:47.941657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.00740v1","created_at":"2026-07-05T03:19:47.941657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.00740","created_at":"2026-07-05T03:19:47.941657+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IKURODK4KU5","created_at":"2026-07-05T03:19:47.941657+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IKURODK4KU5OAEQ","created_at":"2026-07-05T03:19:47.941657+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IKURODK","created_at":"2026-07-05T03:19:47.941657+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.09863","citing_title":"Face De-identification: State-of-the-art Methods and Comparative Studies","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7","json":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7.json","graph_json":"https://pith.science/api/pith-number/5IKURODK4KU5OAEQQOIXH4X6Y7/graph.json","events_json":"https://pith.science/api/pith-number/5IKURODK4KU5OAEQQOIXH4X6Y7/events.json","paper":"https://pith.science/paper/5IKURODK"},"agent_actions":{"view_html":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7","download_json":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7.json","view_paper":"https://pith.science/paper/5IKURODK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.00740&json=true","fetch_graph":"https://pith.science/api/pith-number/5IKURODK4KU5OAEQQOIXH4X6Y7/graph.json","fetch_events":"https://pith.science/api/pith-number/5IKURODK4KU5OAEQQOIXH4X6Y7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7/action/storage_attestation","attest_author":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7/action/author_attestation","sign_citation":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7/action/citation_signature","submit_replication":"https://pith.science/pith/5IKURODK4KU5OAEQQOIXH4X6Y7/action/replication_record"}},"created_at":"2026-07-05T03:19:47.941657+00:00","updated_at":"2026-07-05T03:19:47.941657+00:00"}