{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H5U3GTZKQAXWRVVYRKCQWEILH2","short_pith_number":"pith:H5U3GTZK","schema_version":"1.0","canonical_sha256":"3f69b34f2a802f68d6b88a850b110b3eaa0c0c4ac2078e4dbc7f59cef41caf2b","source":{"kind":"arxiv","id":"2405.02365","version":4},"attestation_state":"computed","paper":{"title":"ModelShield: Adaptive and Robust Watermark against Model Extraction Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chuhan Wu, Kaiyi Pang, Minghu Jiang, Minhao Bai, Tao Qi, Yongfeng Huang","submitted_at":"2024-05-03T06:41:48Z","abstract_excerpt":"Large language models (LLMs) demonstrate general intelligence across a variety of machine learning tasks, thereby enhancing the commercial value of their intellectual property (IP). To protect this IP, model owners typically allow user access only in a black-box manner, however, adversaries can still utilize model extraction attacks to steal the model intelligence encoded in model generation. Watermarking technology offers a promising solution for defending against such attacks by embedding unique identifiers into the model-generated content. However, existing watermarking methods often compro"},"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":"2405.02365","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-05-03T06:41:48Z","cross_cats_sorted":[],"title_canon_sha256":"a6a2547a5475b88e3df9ce48717ac64724893793ad2f71a4cd919cef95e77a6c","abstract_canon_sha256":"2cc08de3e72fe639c10f9bd1aa900b93a7a28a32822e7f0bc6f1279fb838a941"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:46.404152Z","signature_b64":"8K1Yotn+sNzGM8KP69rvLMnYkbXmYGYJHf+135oWE4MaVoFX3A4ih1B0JyIy6VzPm+fKjvTUBO3DlCmYLBiOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f69b34f2a802f68d6b88a850b110b3eaa0c0c4ac2078e4dbc7f59cef41caf2b","last_reissued_at":"2026-07-05T09:59:46.403715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:46.403715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ModelShield: Adaptive and Robust Watermark against Model Extraction Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Chuhan Wu, Kaiyi Pang, Minghu Jiang, Minhao Bai, Tao Qi, Yongfeng Huang","submitted_at":"2024-05-03T06:41:48Z","abstract_excerpt":"Large language models (LLMs) demonstrate general intelligence across a variety of machine learning tasks, thereby enhancing the commercial value of their intellectual property (IP). To protect this IP, model owners typically allow user access only in a black-box manner, however, adversaries can still utilize model extraction attacks to steal the model intelligence encoded in model generation. Watermarking technology offers a promising solution for defending against such attacks by embedding unique identifiers into the model-generated content. However, existing watermarking methods often compro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02365","kind":"arxiv","version":4},"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/2405.02365/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":"2405.02365","created_at":"2026-07-05T09:59:46.403780+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.02365v4","created_at":"2026-07-05T09:59:46.403780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02365","created_at":"2026-07-05T09:59:46.403780+00:00"},{"alias_kind":"pith_short_12","alias_value":"H5U3GTZKQAXW","created_at":"2026-07-05T09:59:46.403780+00:00"},{"alias_kind":"pith_short_16","alias_value":"H5U3GTZKQAXWRVVY","created_at":"2026-07-05T09:59:46.403780+00:00"},{"alias_kind":"pith_short_8","alias_value":"H5U3GTZK","created_at":"2026-07-05T09:59:46.403780+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.15031","citing_title":"A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives","ref_index":159,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2","json":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2.json","graph_json":"https://pith.science/api/pith-number/H5U3GTZKQAXWRVVYRKCQWEILH2/graph.json","events_json":"https://pith.science/api/pith-number/H5U3GTZKQAXWRVVYRKCQWEILH2/events.json","paper":"https://pith.science/paper/H5U3GTZK"},"agent_actions":{"view_html":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2","download_json":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2.json","view_paper":"https://pith.science/paper/H5U3GTZK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.02365&json=true","fetch_graph":"https://pith.science/api/pith-number/H5U3GTZKQAXWRVVYRKCQWEILH2/graph.json","fetch_events":"https://pith.science/api/pith-number/H5U3GTZKQAXWRVVYRKCQWEILH2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2/action/storage_attestation","attest_author":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2/action/author_attestation","sign_citation":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2/action/citation_signature","submit_replication":"https://pith.science/pith/H5U3GTZKQAXWRVVYRKCQWEILH2/action/replication_record"}},"created_at":"2026-07-05T09:59:46.403780+00:00","updated_at":"2026-07-05T09:59:46.403780+00:00"}