{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:K5RG4ZWX3ZZJI27LEO7S2ZQQO4","short_pith_number":"pith:K5RG4ZWX","schema_version":"1.0","canonical_sha256":"57626e66d7de72946beb23bf2d66107724dde2c8f1bdbb28afb62b097938afff","source":{"kind":"arxiv","id":"2407.19524","version":3},"attestation_state":"computed","paper":{"title":"VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hanjun Luo, Haoyu Huang, Ruizhe Chen, Xuecheng Liu, Ziye Deng, Zuozhu Liu","submitted_at":"2024-07-28T16:24:07Z","abstract_excerpt":"With the rapid development of Text-to-Image (T2I) models, biases in human image generation against demographic social groups become a significant concern, impacting fairness and ethical standards in AI. Some researchers propose their methods to tackle with the issue. However, existing methods are designed for specific models with fixed prompts, limiting their adaptability to the fast-evolving models and diverse practical scenarios. Moreover, they neglect the impact of hallucinations, leading to discrepancies between expected and actual results. To address these issues, we introduce VersusDebia"},"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":"2407.19524","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-28T16:24:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9e547b63721969cc6e7ced53c2d943fed57a75a8b839b7ec32a35c640ff28523","abstract_canon_sha256":"c7ae3dc09ce22c0ec2c321246893b64d4a0b5c8ee47ac8f66b7a6ccec2d7e895"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:55:51.586885Z","signature_b64":"hi6grd7TTp/Qb+7Xr4+8R75Y0YXq6hwKa3O2L2EQrFDcKUlnPdCmuXcDbtUNXX7GYwZ6SsXg/3p5RTbo0MRbDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57626e66d7de72946beb23bf2d66107724dde2c8f1bdbb28afb62b097938afff","last_reissued_at":"2026-07-05T08:55:51.586421Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:55:51.586421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hanjun Luo, Haoyu Huang, Ruizhe Chen, Xuecheng Liu, Ziye Deng, Zuozhu Liu","submitted_at":"2024-07-28T16:24:07Z","abstract_excerpt":"With the rapid development of Text-to-Image (T2I) models, biases in human image generation against demographic social groups become a significant concern, impacting fairness and ethical standards in AI. Some researchers propose their methods to tackle with the issue. However, existing methods are designed for specific models with fixed prompts, limiting their adaptability to the fast-evolving models and diverse practical scenarios. Moreover, they neglect the impact of hallucinations, leading to discrepancies between expected and actual results. To address these issues, we introduce VersusDebia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19524","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/2407.19524/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":"2407.19524","created_at":"2026-07-05T08:55:51.586471+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.19524v3","created_at":"2026-07-05T08:55:51.586471+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19524","created_at":"2026-07-05T08:55:51.586471+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5RG4ZWX3ZZJ","created_at":"2026-07-05T08:55:51.586471+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5RG4ZWX3ZZJI27L","created_at":"2026-07-05T08:55:51.586471+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5RG4ZWX","created_at":"2026-07-05T08:55:51.586471+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.30089","citing_title":"Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2509.25346","citing_title":"SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16516","citing_title":"Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11934","citing_title":"BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18167","citing_title":"Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4","json":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4.json","graph_json":"https://pith.science/api/pith-number/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/graph.json","events_json":"https://pith.science/api/pith-number/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/events.json","paper":"https://pith.science/paper/K5RG4ZWX"},"agent_actions":{"view_html":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4","download_json":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4.json","view_paper":"https://pith.science/paper/K5RG4ZWX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.19524&json=true","fetch_graph":"https://pith.science/api/pith-number/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/graph.json","fetch_events":"https://pith.science/api/pith-number/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/action/storage_attestation","attest_author":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/action/author_attestation","sign_citation":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/action/citation_signature","submit_replication":"https://pith.science/pith/K5RG4ZWX3ZZJI27LEO7S2ZQQO4/action/replication_record"}},"created_at":"2026-07-05T08:55:51.586471+00:00","updated_at":"2026-07-05T08:55:51.586471+00:00"}