{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VL2X7ARLSARPTDJFP7C26DN2QZ","short_pith_number":"pith:VL2X7ARL","schema_version":"1.0","canonical_sha256":"aaf57f822b9022f98d257fc5af0dba867de58f8f66a33d62cf19de8af6c1d5ee","source":{"kind":"arxiv","id":"2312.05476","version":3},"attestation_state":"computed","paper":{"title":"Exploring the Naturalness of AI-Generated Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengyu Sun, Guangtao Zhai, Haoning Wu, Jun Jia, Shangling Jui, Wei Sun, Wenjun Zhang, Xiongkuo Min, Zhongpeng Ji, Zicheng Zhang, Zijian Chen","submitted_at":"2023-12-09T06:08:09Z","abstract_excerpt":"The proliferation of Artificial Intelligence-Generated Images (AGIs) has greatly expanded the Image Naturalness Assessment (INA) problem. Different from early definitions that mainly focus on tone-mapped images with limited distortions (e.g., exposure, contrast, and color reproduction), INA on AI-generated images is especially challenging as it has more diverse contents and could be affected by factors from multiple perspectives, including low-level technical distortions and high-level rationality distortions. In this paper, we take the first step to benchmark and assess the visual naturalness"},"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":"2312.05476","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-09T06:08:09Z","cross_cats_sorted":[],"title_canon_sha256":"7cc6285e78d50f24a1c80c37e553cb29cd8c210076fb1d2388830d865fb72474","abstract_canon_sha256":"7dca90d38d2ca85ee2a31f70954bc2037a99aa7830ab40545ff10709c1d5f9f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:36.490426Z","signature_b64":"j52CBqiX9LvpeMYf3nU4n9J8mDNp4+BF+Idru48izqNxefIR7QwDFh4YnbE16B1BO5izLU/70/QpLx9uDQtrDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaf57f822b9022f98d257fc5af0dba867de58f8f66a33d62cf19de8af6c1d5ee","last_reissued_at":"2026-07-05T07:51:36.489830Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:36.489830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Naturalness of AI-Generated Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengyu Sun, Guangtao Zhai, Haoning Wu, Jun Jia, Shangling Jui, Wei Sun, Wenjun Zhang, Xiongkuo Min, Zhongpeng Ji, Zicheng Zhang, Zijian Chen","submitted_at":"2023-12-09T06:08:09Z","abstract_excerpt":"The proliferation of Artificial Intelligence-Generated Images (AGIs) has greatly expanded the Image Naturalness Assessment (INA) problem. Different from early definitions that mainly focus on tone-mapped images with limited distortions (e.g., exposure, contrast, and color reproduction), INA on AI-generated images is especially challenging as it has more diverse contents and could be affected by factors from multiple perspectives, including low-level technical distortions and high-level rationality distortions. In this paper, we take the first step to benchmark and assess the visual naturalness"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05476","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/2312.05476/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":"2312.05476","created_at":"2026-07-05T07:51:36.489903+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.05476v3","created_at":"2026-07-05T07:51:36.489903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05476","created_at":"2026-07-05T07:51:36.489903+00:00"},{"alias_kind":"pith_short_12","alias_value":"VL2X7ARLSARP","created_at":"2026-07-05T07:51:36.489903+00:00"},{"alias_kind":"pith_short_16","alias_value":"VL2X7ARLSARPTDJF","created_at":"2026-07-05T07:51:36.489903+00:00"},{"alias_kind":"pith_short_8","alias_value":"VL2X7ARL","created_at":"2026-07-05T07:51:36.489903+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00931","citing_title":"CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ","json":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ.json","graph_json":"https://pith.science/api/pith-number/VL2X7ARLSARPTDJFP7C26DN2QZ/graph.json","events_json":"https://pith.science/api/pith-number/VL2X7ARLSARPTDJFP7C26DN2QZ/events.json","paper":"https://pith.science/paper/VL2X7ARL"},"agent_actions":{"view_html":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ","download_json":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ.json","view_paper":"https://pith.science/paper/VL2X7ARL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.05476&json=true","fetch_graph":"https://pith.science/api/pith-number/VL2X7ARLSARPTDJFP7C26DN2QZ/graph.json","fetch_events":"https://pith.science/api/pith-number/VL2X7ARLSARPTDJFP7C26DN2QZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ/action/storage_attestation","attest_author":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ/action/author_attestation","sign_citation":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ/action/citation_signature","submit_replication":"https://pith.science/pith/VL2X7ARLSARPTDJFP7C26DN2QZ/action/replication_record"}},"created_at":"2026-07-05T07:51:36.489903+00:00","updated_at":"2026-07-05T07:51:36.489903+00:00"}