{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DJJDSQBDV75OGDC3TM7FP6G7EO","short_pith_number":"pith:DJJDSQBD","schema_version":"1.0","canonical_sha256":"1a52394023affae30c5b9b3e57f8df23834f0dbce0604c191d782412dcc042ef","source":{"kind":"arxiv","id":"2302.11562","version":1},"attestation_state":"computed","paper":{"title":"Uncovering Bias in Face Generation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Adriano Koshiyama, Cristian Mu\\~noz, Sara Zannone, Umar Mohammed","submitted_at":"2023-02-22T18:57:35Z","abstract_excerpt":"Recent advancements in GANs and diffusion models have enabled the creation of high-resolution, hyper-realistic images. However, these models may misrepresent certain social groups and present bias. Understanding bias in these models remains an important research question, especially for tasks that support critical decision-making and could affect minorities. The contribution of this work is a novel analysis covering architectures and embedding spaces for fine-grained understanding of bias over three approaches: generators, attribute modifier, and post-processing bias mitigators. This work show"},"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":"2302.11562","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-02-22T18:57:35Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5e9fab85e813c84c52ef62859f9bf53b76a2f4d9cf756a44b806e973fb2ef443","abstract_canon_sha256":"3348681994d050c095cb23c6ed3f1adf682e4273c209b091c660b3684eae7e2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:44.865285Z","signature_b64":"JXvFuE4trT3KWVZwOGWxg3ERCUVVz9rEeKMT/P3vP+JmkVrHGfD97xSbl7AOBx4JLGVa4mkwrCGk/KLCjR+pCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a52394023affae30c5b9b3e57f8df23834f0dbce0604c191d782412dcc042ef","last_reissued_at":"2026-07-05T05:44:44.864894Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:44.864894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncovering Bias in Face Generation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Adriano Koshiyama, Cristian Mu\\~noz, Sara Zannone, Umar Mohammed","submitted_at":"2023-02-22T18:57:35Z","abstract_excerpt":"Recent advancements in GANs and diffusion models have enabled the creation of high-resolution, hyper-realistic images. However, these models may misrepresent certain social groups and present bias. Understanding bias in these models remains an important research question, especially for tasks that support critical decision-making and could affect minorities. The contribution of this work is a novel analysis covering architectures and embedding spaces for fine-grained understanding of bias over three approaches: generators, attribute modifier, and post-processing bias mitigators. This work show"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11562","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/2302.11562/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":"2302.11562","created_at":"2026-07-05T05:44:44.864952+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.11562v1","created_at":"2026-07-05T05:44:44.864952+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11562","created_at":"2026-07-05T05:44:44.864952+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJJDSQBDV75O","created_at":"2026-07-05T05:44:44.864952+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJJDSQBDV75OGDC3","created_at":"2026-07-05T05:44:44.864952+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJJDSQBD","created_at":"2026-07-05T05:44:44.864952+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11615","citing_title":"Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2508.20640","citing_title":"CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO","json":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO.json","graph_json":"https://pith.science/api/pith-number/DJJDSQBDV75OGDC3TM7FP6G7EO/graph.json","events_json":"https://pith.science/api/pith-number/DJJDSQBDV75OGDC3TM7FP6G7EO/events.json","paper":"https://pith.science/paper/DJJDSQBD"},"agent_actions":{"view_html":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO","download_json":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO.json","view_paper":"https://pith.science/paper/DJJDSQBD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.11562&json=true","fetch_graph":"https://pith.science/api/pith-number/DJJDSQBDV75OGDC3TM7FP6G7EO/graph.json","fetch_events":"https://pith.science/api/pith-number/DJJDSQBDV75OGDC3TM7FP6G7EO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO/action/storage_attestation","attest_author":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO/action/author_attestation","sign_citation":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO/action/citation_signature","submit_replication":"https://pith.science/pith/DJJDSQBDV75OGDC3TM7FP6G7EO/action/replication_record"}},"created_at":"2026-07-05T05:44:44.864952+00:00","updated_at":"2026-07-05T05:44:44.864952+00:00"}