Lost in Overlap: Exploring Watermark Collision in LLMs
Yiyang Luo Ke Lin Chao Gu
The proliferation of large language models (LLMs) in generating content
raises concerns about text copyright. Watermarking methods, particularly
logit-based approaches, embed imperceptible identifiers into text to address
these challenges. However, the widespread use of watermarking across diverse
LLMs has led to an inevitable issue known as watermark collision during common
tasks like question answering and paraphrasing. This study focuses on dual
watermark collisions, where two watermarks are present simultaneously in the
same text. The research demonstrates that watermark collision poses a threat to
detection performance for detectors of both upstream and downstream watermark
algorithms.