{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CFUXE4EQ2DKMW2DCEOOVFYA7U3","short_pith_number":"pith:CFUXE4EQ","schema_version":"1.0","canonical_sha256":"1169727090d0d4cb6862239d52e01fa6cd457e44abce29beb3dbb0a07e78a3cb","source":{"kind":"arxiv","id":"2607.22745","version":1},"attestation_state":"computed","paper":{"title":"AI-generated Images Challenge Visual Trust in High-risk Scenarios","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Linan Yue, Min-Ling Zhang, Pengfei Fang, Shimin Di, WeiBo Gao, Yichao Du, Yichen Xiao, Yi-Zhi Wang","submitted_at":"2026-07-23T06:56:49Z","abstract_excerpt":"Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing detection benchmarks rarely examine synthetic images in public- and individual-safety contexts, where misleading visual content may carry substantial risks. Here we introduce SafeIMG, a safety-oriented benchmark spanning 12 public- and individual-safety scenarios generated using GPT Image 2. Unlike benchmarks centred on generic imagery and image-level labels, SafeIMG evaluates not only whether detectors recognise sy"},"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":"2607.22745","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T06:56:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3bd2f0b510f86babb0a6685cbbfdb8b6d159f503971444ad5d5a46cd97913baf","abstract_canon_sha256":"ce5846712ccecec57dcc3bb35d2851c9f98a0501199493613ac4fd5941694743"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:52.258295Z","signature_b64":"VRvBnuAO2GX2j8cqpsVSq3oyVTsbm3CICXJgDrgdePqFN81ZT4mwHLjr2LLVIS+eVmXbJlReiwbRJFXEGDgBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1169727090d0d4cb6862239d52e01fa6cd457e44abce29beb3dbb0a07e78a3cb","last_reissued_at":"2026-07-28T00:21:52.257423Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:52.257423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI-generated Images Challenge Visual Trust in High-risk Scenarios","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Linan Yue, Min-Ling Zhang, Pengfei Fang, Shimin Di, WeiBo Gao, Yichao Du, Yichen Xiao, Yi-Zhi Wang","submitted_at":"2026-07-23T06:56:49Z","abstract_excerpt":"Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing detection benchmarks rarely examine synthetic images in public- and individual-safety contexts, where misleading visual content may carry substantial risks. Here we introduce SafeIMG, a safety-oriented benchmark spanning 12 public- and individual-safety scenarios generated using GPT Image 2. Unlike benchmarks centred on generic imagery and image-level labels, SafeIMG evaluates not only whether detectors recognise sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22745","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/2607.22745/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":"2607.22745","created_at":"2026-07-28T00:21:52.257847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22745v1","created_at":"2026-07-28T00:21:52.257847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22745","created_at":"2026-07-28T00:21:52.257847+00:00"},{"alias_kind":"pith_short_12","alias_value":"CFUXE4EQ2DKM","created_at":"2026-07-28T00:21:52.257847+00:00"},{"alias_kind":"pith_short_16","alias_value":"CFUXE4EQ2DKMW2DC","created_at":"2026-07-28T00:21:52.257847+00:00"},{"alias_kind":"pith_short_8","alias_value":"CFUXE4EQ","created_at":"2026-07-28T00:21:52.257847+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3","json":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3.json","graph_json":"https://pith.science/api/pith-number/CFUXE4EQ2DKMW2DCEOOVFYA7U3/graph.json","events_json":"https://pith.science/api/pith-number/CFUXE4EQ2DKMW2DCEOOVFYA7U3/events.json","paper":"https://pith.science/paper/CFUXE4EQ"},"agent_actions":{"view_html":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3","download_json":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3.json","view_paper":"https://pith.science/paper/CFUXE4EQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22745&json=true","fetch_graph":"https://pith.science/api/pith-number/CFUXE4EQ2DKMW2DCEOOVFYA7U3/graph.json","fetch_events":"https://pith.science/api/pith-number/CFUXE4EQ2DKMW2DCEOOVFYA7U3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3/action/storage_attestation","attest_author":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3/action/author_attestation","sign_citation":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3/action/citation_signature","submit_replication":"https://pith.science/pith/CFUXE4EQ2DKMW2DCEOOVFYA7U3/action/replication_record"}},"created_at":"2026-07-28T00:21:52.257847+00:00","updated_at":"2026-07-28T00:21:52.257847+00:00"}