  Recently, text watermarking algorithms for large language models (LLMs) have
been proposed to mitigate the potential harms of text generated by LLMs,
including fake news and copyright issues. However, current watermark detection
algorithms require the secret key used in the watermark generation process,
making them susceptible to security breaches and counterfeiting during public
detection. To address this limitation, we propose an unforgeable publicly
verifiable watermark algorithm named UPV that uses two different neural
networks for watermark generation and detection, instead of using the same key
at both stages. Meanwhile, the token embedding parameters are shared between
the generation and detection networks, which makes the detection network
achieve a high accuracy very efficiently. Experiments demonstrate that our
algorithm attains high detection accuracy and computational efficiency through
neural networks. Subsequent analysis confirms the high complexity involved in
forging the watermark from the detection network. Our code is available at
\href{https://github.com/THU-BPM/unforgeable_watermark}{https://github.com/THU-BPM/unforgeable\_watermark}.
Additionally, our algorithm could also be accessed through MarkLLM
\citep{pan2024markllm} \footnote{https://github.com/THU-BPM/MarkLLM}.
