Are You Copying My Prompt? Protecting the Copyright of Vision Prompt for VPaaS via Watermark
Huali Ren, Anli Yan, Chong-zhi Gao, Hongyang Yan, Zhenxin Zhang, Jin, Li

TL;DR
This paper introduces WVPrompt, a watermarking method for visual prompts in VPaaS that protects intellectual property by embedding and verifying ownership without compromising prompt functionality.
Contribution
It proposes a novel black-box watermarking technique for visual prompts using backdoor attacks and hypothesis testing for ownership verification.
Findings
WVPrompt effectively embeds watermarks into prompts.
The method is robust against adversarial modifications.
Experiments show high accuracy in watermark verification.
Abstract
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security
Methodstravel james
