{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EXSPSQ35EF5WI5TPNJDLXO6KQU","short_pith_number":"pith:EXSPSQ35","schema_version":"1.0","canonical_sha256":"25e4f9437d217b64766f6a46bbbbca851bf3d594efc5fbafc82598f910e1768d","source":{"kind":"arxiv","id":"2402.11843","version":1},"attestation_state":"computed","paper":{"title":"WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianfu Zhang, Yan Hong","submitted_at":"2024-02-19T05:13:39Z","abstract_excerpt":"The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred"},"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":"2402.11843","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-19T05:13:39Z","cross_cats_sorted":[],"title_canon_sha256":"78a615285f9fd8475300fd8c4830f9b7c3b3c7d5b10351c3ae912b06a48e68fa","abstract_canon_sha256":"7a15d621569044c908892395b4df78f7a72f87d1e7c2f8d7044305f05d1cb1dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:45.229355Z","signature_b64":"a2mOzQQCv0f9FzdBSHL5Gr5nZoEJMv5hEohJRND7jhmYRoXe+r7Uds6X1vq3qCq3UN33Z+Z4BeJ8+OVUWNNTBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25e4f9437d217b64766f6a46bbbbca851bf3d594efc5fbafc82598f910e1768d","last_reissued_at":"2026-07-05T07:46:45.228786Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:45.228786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianfu Zhang, Yan Hong","submitted_at":"2024-02-19T05:13:39Z","abstract_excerpt":"The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11843","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/2402.11843/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":"2402.11843","created_at":"2026-07-05T07:46:45.228840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11843v1","created_at":"2026-07-05T07:46:45.228840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11843","created_at":"2026-07-05T07:46:45.228840+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXSPSQ35EF5W","created_at":"2026-07-05T07:46:45.228840+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXSPSQ35EF5WI5TP","created_at":"2026-07-05T07:46:45.228840+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXSPSQ35","created_at":"2026-07-05T07:46:45.228840+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19259","citing_title":"TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00606","citing_title":"FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2509.21864","citing_title":"Deepfakes: we need to re-think the concept of \"real\" images","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12967","citing_title":"ImageAttributionBench: How Far Are We from Generalizable Attribution?","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08226","citing_title":"SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01638","citing_title":"Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04445","citing_title":"LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU","json":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU.json","graph_json":"https://pith.science/api/pith-number/EXSPSQ35EF5WI5TPNJDLXO6KQU/graph.json","events_json":"https://pith.science/api/pith-number/EXSPSQ35EF5WI5TPNJDLXO6KQU/events.json","paper":"https://pith.science/paper/EXSPSQ35"},"agent_actions":{"view_html":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU","download_json":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU.json","view_paper":"https://pith.science/paper/EXSPSQ35","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11843&json=true","fetch_graph":"https://pith.science/api/pith-number/EXSPSQ35EF5WI5TPNJDLXO6KQU/graph.json","fetch_events":"https://pith.science/api/pith-number/EXSPSQ35EF5WI5TPNJDLXO6KQU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU/action/storage_attestation","attest_author":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU/action/author_attestation","sign_citation":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU/action/citation_signature","submit_replication":"https://pith.science/pith/EXSPSQ35EF5WI5TPNJDLXO6KQU/action/replication_record"}},"created_at":"2026-07-05T07:46:45.228840+00:00","updated_at":"2026-07-05T07:46:45.228840+00:00"}