{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NZJP2OY4PNQSOXY4S62UZPXTAD","short_pith_number":"pith:NZJP2OY4","schema_version":"1.0","canonical_sha256":"6e52fd3b1c7b61275f1c97b54cbef300fc9e81c2437f8477841eaa8a7db08fe0","source":{"kind":"arxiv","id":"2307.16230","version":7},"attestation_state":"computed","paper":{"title":"An Unforgeable Publicly Verifiable Watermark for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aiwei Liu, Irwin King, Leyi Pan, Lijie Wen, Philip S. Yu, Shu'ang Li, Xuming Hu","submitted_at":"2023-07-30T13:43:27Z","abstract_excerpt":"Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyright issues. However, current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection. To address this limitation, we propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the s"},"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":"2307.16230","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-30T13:43:27Z","cross_cats_sorted":[],"title_canon_sha256":"a4af310f126df823b77e584050c6d492cc6a8e4eccdea80bca991de43e03b89d","abstract_canon_sha256":"72c7b948ed4ae67af2c00c21b0d7b553d69a5d1b9644effe9e881098f5353982"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:10.815124Z","signature_b64":"3bsrNU6ry1te87i59cGHWAUtLtszIUu2nPitaImoe8mORSnaTP1SEVnqkMjdIZHLl/EvrxU3luc0QmW0K+LgAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e52fd3b1c7b61275f1c97b54cbef300fc9e81c2437f8477841eaa8a7db08fe0","last_reissued_at":"2026-07-05T08:23:10.814624Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:10.814624Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Unforgeable Publicly Verifiable Watermark for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aiwei Liu, Irwin King, Leyi Pan, Lijie Wen, Philip S. Yu, Shu'ang Li, Xuming Hu","submitted_at":"2023-07-30T13:43:27Z","abstract_excerpt":"Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyright issues. However, current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection. To address this limitation, we propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.16230","kind":"arxiv","version":7},"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/2307.16230/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":"2307.16230","created_at":"2026-07-05T08:23:10.814683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.16230v7","created_at":"2026-07-05T08:23:10.814683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.16230","created_at":"2026-07-05T08:23:10.814683+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZJP2OY4PNQS","created_at":"2026-07-05T08:23:10.814683+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZJP2OY4PNQSOXY4","created_at":"2026-07-05T08:23:10.814683+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZJP2OY4","created_at":"2026-07-05T08:23:10.814683+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20835","citing_title":"PromptMark: A Prompt-Guided Iterative-Feedback Framework for Source Code Watermarking","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25967","citing_title":"Hidden in Plain Tokens: Simply Robust, Gradient-Free Watermark for Synthetic Audio","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25796","citing_title":"SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2509.20924","citing_title":"RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2510.18333","citing_title":"Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12216","citing_title":"TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08759","citing_title":"Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD","json":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD.json","graph_json":"https://pith.science/api/pith-number/NZJP2OY4PNQSOXY4S62UZPXTAD/graph.json","events_json":"https://pith.science/api/pith-number/NZJP2OY4PNQSOXY4S62UZPXTAD/events.json","paper":"https://pith.science/paper/NZJP2OY4"},"agent_actions":{"view_html":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD","download_json":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD.json","view_paper":"https://pith.science/paper/NZJP2OY4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.16230&json=true","fetch_graph":"https://pith.science/api/pith-number/NZJP2OY4PNQSOXY4S62UZPXTAD/graph.json","fetch_events":"https://pith.science/api/pith-number/NZJP2OY4PNQSOXY4S62UZPXTAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD/action/storage_attestation","attest_author":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD/action/author_attestation","sign_citation":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD/action/citation_signature","submit_replication":"https://pith.science/pith/NZJP2OY4PNQSOXY4S62UZPXTAD/action/replication_record"}},"created_at":"2026-07-05T08:23:10.814683+00:00","updated_at":"2026-07-05T08:23:10.814683+00:00"}