Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions

• URL: http://arxiv.org/abs/2406.02603v1
• Date: Sun, 2 Jun 2024 04:07:32 GMT
• Title: Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions
• Authors: Yihan Wu, Ruibo Chen, Zhengmian Hu, Yanshuo Chen, Junfeng Guo, Hongyang Zhang, Heng Huang,
• Abstract summary: Language model (LM) watermarking techniques inject a statistical signal into LM-generated content. We introduce a new family of distortion-free watermarks--beta-watermark. Experimental results support that the beta-watermark can effectively reduce the distribution bias under key collisions.
• Score: 58.777395817878514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language model (LM) watermarking techniques inject a statistical signal into LM-generated content by substituting the random sampling process with pseudo-random sampling, using watermark keys as the random seed. Among these statistical watermarking approaches, distortion-free watermarks are particularly crucial because they embed watermarks into LM-generated content without compromising generation quality. However, one notable limitation of pseudo-random sampling compared to true-random sampling is that, under the same watermark keys (i.e., key collision), the results of pseudo-random sampling exhibit correlations. This limitation could potentially undermine the distortion-free property. Our studies reveal that key collisions are inevitable due to the limited availability of watermark keys, and existing distortion-free watermarks exhibit a significant distribution bias toward the original LM distribution in the presence of key collisions. Moreover, achieving a perfect distortion-free watermark is impossible as no statistical signal can be embedded under key collisions. To reduce the distribution bias caused by key collisions, we introduce a new family of distortion-free watermarks--beta-watermark. Experimental results support that the beta-watermark can effectively reduce the distribution bias under key collisions.

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