{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EVAWSR4KF6PPV72Z6VLIEFLSPG","short_pith_number":"pith:EVAWSR4K","schema_version":"1.0","canonical_sha256":"254169478a2f9efaff59f556821572798d6ca3e06ea0c56e40ece33962c8b123","source":{"kind":"arxiv","id":"2312.00195","version":2},"attestation_state":"computed","paper":{"title":"Raising the Bar of AI-generated Image Detection with CLIP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva, Matthias Nie{\\ss}ner, Riccardo Corvi","submitted_at":"2023-11-30T21:11:20Z","abstract_excerpt":"The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits surprising generalization ability and high robustness across di"},"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":"2312.00195","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-30T21:11:20Z","cross_cats_sorted":[],"title_canon_sha256":"83fbe721aea2682d14425174adb0fa15e131004d88b4c72abfbb5e6875cd5162","abstract_canon_sha256":"e62f4fe58dde25cbb42612a6d24b39d17996555bbea3089602535614cf9e83a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:10.476167Z","signature_b64":"MUV0Xv5tE5tTH452ru9q2wPa2OuVfn2F33as5kzVdTbPd9vT31Rwrn61qlUIkFeCNZ4EK3lpWQpMw9sJ2AQtDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"254169478a2f9efaff59f556821572798d6ca3e06ea0c56e40ece33962c8b123","last_reissued_at":"2026-07-05T08:13:10.475734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:10.475734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Raising the Bar of AI-generated Image Detection with CLIP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva, Matthias Nie{\\ss}ner, Riccardo Corvi","submitted_at":"2023-11-30T21:11:20Z","abstract_excerpt":"The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits surprising generalization ability and high robustness across di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00195","kind":"arxiv","version":2},"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/2312.00195/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":"2312.00195","created_at":"2026-07-05T08:13:10.475791+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.00195v2","created_at":"2026-07-05T08:13:10.475791+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00195","created_at":"2026-07-05T08:13:10.475791+00:00"},{"alias_kind":"pith_short_12","alias_value":"EVAWSR4KF6PP","created_at":"2026-07-05T08:13:10.475791+00:00"},{"alias_kind":"pith_short_16","alias_value":"EVAWSR4KF6PPV72Z","created_at":"2026-07-05T08:13:10.475791+00:00"},{"alias_kind":"pith_short_8","alias_value":"EVAWSR4K","created_at":"2026-07-05T08:13:10.475791+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06254","citing_title":"VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection","ref_index":32,"is_internal_anchor":true},{"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":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28510","citing_title":"Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14799","citing_title":"Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2507.10236","citing_title":"Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04445","citing_title":"LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG","json":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG.json","graph_json":"https://pith.science/api/pith-number/EVAWSR4KF6PPV72Z6VLIEFLSPG/graph.json","events_json":"https://pith.science/api/pith-number/EVAWSR4KF6PPV72Z6VLIEFLSPG/events.json","paper":"https://pith.science/paper/EVAWSR4K"},"agent_actions":{"view_html":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG","download_json":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG.json","view_paper":"https://pith.science/paper/EVAWSR4K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.00195&json=true","fetch_graph":"https://pith.science/api/pith-number/EVAWSR4KF6PPV72Z6VLIEFLSPG/graph.json","fetch_events":"https://pith.science/api/pith-number/EVAWSR4KF6PPV72Z6VLIEFLSPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG/action/storage_attestation","attest_author":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG/action/author_attestation","sign_citation":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG/action/citation_signature","submit_replication":"https://pith.science/pith/EVAWSR4KF6PPV72Z6VLIEFLSPG/action/replication_record"}},"created_at":"2026-07-05T08:13:10.475791+00:00","updated_at":"2026-07-05T08:13:10.475791+00:00"}