Unforgeable Watermarks for Language Models via Robust Signatures
Huijia Lin, Kameron Shahabi, Min Jae Song

TL;DR
This paper introduces a novel watermarking scheme for language models that guarantees unforgeability and recoverability, enabling secure content attribution and traceability even under adversarial conditions.
Contribution
It presents the first undetectable, robust watermarking scheme with unforgeability and recoverability, utilizing cryptographic primitives like robust digital signatures for language models.
Findings
Scheme is robust against substitutions and perturbations.
Provides unforgeability, preventing false positives.
Enables source identification for watermarked content.
Abstract
Language models now routinely produce text that is difficult to distinguish from human writing, raising the need for robust tools to verify content provenance. Watermarking has emerged as a promising countermeasure, with existing work largely focused on model quality preservation and robust detection. However, current schemes provide limited protection against false attribution. We strengthen the notion of soundness by introducing two novel guarantees: unforgeability and recoverability. Unforgeability prevents adversaries from crafting false positives, texts that are far from any output from the watermarked model but are nonetheless flagged as watermarked. Recoverability provides an additional layer of protection: whenever a watermark is detected, the detector identifies the source text from which the flagged content was derived. Together, these properties strengthen content ownership…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Generative Adversarial Networks and Image Synthesis · Big Data and Digital Economy
