  Large language models (LLMs) have demonstrated outstanding performance,
making them valuable digital assets with significant commercial potential.
Unfortunately, the LLM and its API are susceptible to intellectual property
theft. Watermarking is a classic solution for copyright verification. However,
most recent emerging LLM watermarking methods focus on identifying AI-generated
texts rather than watermarking LLM itself. Only a few attempts are based on
weight quantification and backdoor watermarking, which are not robust or covert
enough, limiting their applicability in practice.
  To address this issue, we propose a novel watermarking method for LLMs based
on knowledge injection and innovatively use knowledge as the watermark carrier.
Specifically, in the watermark embedding stage, we first embed the watermarks
into the selected knowledge to obtain the watermarked knowledge, subsequently
injected into the to-be-protected LLM. In the watermark extraction stage,
questions related to the watermarked knowledge are designed, for querying the
suspect LLM and extracting the watermarks from its response. The experiments
show that the watermark extraction success rate is close to 100% and
demonstrate the effectiveness, fidelity, stealthiness, and robustness of our
proposed method.
