  Visual Prompt Learning (VPL) differs from traditional fine-tuning methods in
reducing significant resource consumption by avoiding updating pre-trained
model parameters. Instead, it focuses on learning an input perturbation, a
visual prompt, added to downstream task data for making predictions. Since
learning generalizable prompts requires expert design and creation, which is
technically demanding and time-consuming in the optimization process,
developers of Visual Prompts as a Service (VPaaS) have emerged. These
developers profit by providing well-crafted prompts to authorized customers.
However, a significant drawback is that prompts can be easily copied and
redistributed, threatening the intellectual property of VPaaS developers.
Hence, there is an urgent need for technology to protect the rights of VPaaS
developers. To this end, we present a method named \textbf{WVPrompt} that
employs visual prompt watermarking in a black-box way. WVPrompt consists of two
parts: prompt watermarking and prompt verification. Specifically, it utilizes a
poison-only backdoor attack method to embed a watermark into the prompt and
then employs a hypothesis-testing approach for remote verification of prompt
ownership. Extensive experiments have been conducted on three well-known
benchmark datasets using three popular pre-trained models: RN50, BIT-M, and
Instagram. The experimental results demonstrate that WVPrompt is efficient,
harmless, and robust to various adversarial operations.
