{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5GTEEVRCZ4USSCKD5X6ZS6QQBK","short_pith_number":"pith:5GTEEVRC","schema_version":"1.0","canonical_sha256":"e9a6425622cf29290943edfd997a100a9cb83084a9388739dcf34f4a4105080e","source":{"kind":"arxiv","id":"2606.03348","version":1},"attestation_state":"computed","paper":{"title":"SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Haoran Liu, Hongning Wang, Junxiao Yang, Minghao Zhang, Minlie Huang, Shiyao Cui, Xiaoce Wang","submitted_at":"2026-06-02T08:57:38Z","abstract_excerpt":"Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that existing systems remain unreliable: under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% true positive rate (TPR), open-source AIGC detectors"},"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":"2606.03348","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-02T08:57:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"27752ddf03399581be5ffe643e9efbf63e5218682ce6035f16d5323368e70dca","abstract_canon_sha256":"37a6241f33c91bbe7fab6725ad86aabdddb682643c052d3cac605e84ab173049"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:55.660744Z","signature_b64":"D8nSKLCvQ5b/zREofeRbiqRByfg0r6H+fGHYDbXfCOr/3CJdBbPRNmj5p8NRON8jkOoYrrqjA3At/S/cl3dKDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9a6425622cf29290943edfd997a100a9cb83084a9388739dcf34f4a4105080e","last_reissued_at":"2026-06-03T01:05:55.660346Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:55.660346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Haoran Liu, Hongning Wang, Junxiao Yang, Minghao Zhang, Minlie Huang, Shiyao Cui, Xiaoce Wang","submitted_at":"2026-06-02T08:57:38Z","abstract_excerpt":"Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that existing systems remain unreliable: under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% true positive rate (TPR), open-source AIGC detectors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03348","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/2606.03348/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":"2606.03348","created_at":"2026-06-03T01:05:55.660405+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03348v1","created_at":"2026-06-03T01:05:55.660405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03348","created_at":"2026-06-03T01:05:55.660405+00:00"},{"alias_kind":"pith_short_12","alias_value":"5GTEEVRCZ4US","created_at":"2026-06-03T01:05:55.660405+00:00"},{"alias_kind":"pith_short_16","alias_value":"5GTEEVRCZ4USSCKD","created_at":"2026-06-03T01:05:55.660405+00:00"},{"alias_kind":"pith_short_8","alias_value":"5GTEEVRC","created_at":"2026-06-03T01:05:55.660405+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/5GTEEVRCZ4USSCKD5X6ZS6QQBK","json":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK.json","graph_json":"https://pith.science/api/pith-number/5GTEEVRCZ4USSCKD5X6ZS6QQBK/graph.json","events_json":"https://pith.science/api/pith-number/5GTEEVRCZ4USSCKD5X6ZS6QQBK/events.json","paper":"https://pith.science/paper/5GTEEVRC"},"agent_actions":{"view_html":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK","download_json":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK.json","view_paper":"https://pith.science/paper/5GTEEVRC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03348&json=true","fetch_graph":"https://pith.science/api/pith-number/5GTEEVRCZ4USSCKD5X6ZS6QQBK/graph.json","fetch_events":"https://pith.science/api/pith-number/5GTEEVRCZ4USSCKD5X6ZS6QQBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK/action/storage_attestation","attest_author":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK/action/author_attestation","sign_citation":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK/action/citation_signature","submit_replication":"https://pith.science/pith/5GTEEVRCZ4USSCKD5X6ZS6QQBK/action/replication_record"}},"created_at":"2026-06-03T01:05:55.660405+00:00","updated_at":"2026-06-03T01:05:55.660405+00:00"}