DiPmark: A Stealthy, Efficient and Resilient Watermark for Large
  Language Models
Yihan Wu Zhengmian Hu Hongyang Zhang Heng Huang
Watermarking techniques offer a promising way to secure data via embedding
covert information into the data. A paramount challenge in the domain lies in
preserving the distribution of original data during watermarking. Our research
extends and refines existing watermarking framework, placing emphasis on the
importance of a distribution-preserving (DiP) watermark. Contrary to the
current strategies, our proposed DiPmark preserves the original token
distribution during watermarking (stealthy), is detectable without access to
the language model API or weights (efficient), and is robust to moderate
changes of tokens (resilient). This is achieved by incorporating a novel
reweight strategy, combined with a hash function that assigns unique
\textit{i.i.d.} ciphers based on the context. The empirical benchmarks of our
approach underscore its stealthiness, efficiency, and resilience, making it a
robust solution for watermarking tasks that demand impeccable quality
preservation.