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Publicly-Detectable Watermarking for Language Models
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We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LM output using rejection sampling and prove that this produces unforgeable and distortion-free (i.e., undetectable without access to the public key) text output. We make use of error-correction to overcome periods of low entropy, a barrier for all prior watermarking schemes. We implement our scheme and find that our formal claims are met in practice.
Forward citations
Cited by 4 Pith papers
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Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.
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GaussMark: A Practical Approach for Structural Watermarking of Language Models
GaussMark embeds a detectable watermark by adding per-generation Gaussian noise to one weight matrix and detecting gradient alignment with that noise.
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SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness
SAMark uses self-anchored semantic green regions, multi-channel hyperbolic scoring, and diversity-aware filtering to reach 90.2% TP@FP1% detection under paragraph paraphrasing while preserving text quality.
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A Survey of LLM $\times$ DATA
A comprehensive survey of the bidirectional links between LLMs and data management, organized as DATA4LLM and LLM4DATA with a new 'IaaS' data-quality framework.
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