🤖 AI Summary
This work proposes the first watermarking mechanism for language models that simultaneously achieves unforgeability, recoverability, robustness, and undetectability. Existing approaches are vulnerable to false attribution attacks, wherein adversaries generate watermark-free text that is erroneously flagged as watermarked. To address this, the authors introduce a robust (recoverable) digital signature scheme based on attribute-preserving hash functions, enabling effective verification of texts that are semantically or syntactically close to the original while preventing forgery from distant inputs. The method guarantees that any detected watermark must originate from a specific model and can be traced back to its exact source generation, thereby significantly enhancing the reliability of content attribution and enabling fine-grained provenance tracking for generated text.
📝 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 by linking content exclusively to its generating model, enabling secure attribution and fine-grained traceability. We construct the first undetectable watermarking scheme that is robust, unforgeable, and recoverable with respect to substitutions (i.e., perturbations in Hamming metric). The key technical ingredient is a new cryptographic primitive called robust (or recoverable) digital signatures, which allow verification of messages that are close to signed ones, while preventing forgery of messages that are far from all previously signed messages. We show that any standard digital signature scheme can be boosted to a robust one using property-preserving hash functions (Boyle, LaVigne, and Vaikuntanathan, ITCS 2019).