Labels Predicted by AI
Prompt Injection Watermarking Technology Intellectual Property Protection
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
Abstract
In-Context Learning (ICL) and efficient fine-tuning methods significantly enhanced the efficiency of applying Large Language Models (LLMs) to downstream tasks. However, they also raise concerns about the imitation and infringement of personal creative data. Current methods for data copyright protection primarily focuses on content security but lacks effectiveness in protecting the copyrights of text styles. In this paper, we introduce a novel implicit zero-watermarking scheme, namely MiZero. This scheme establishes a precise watermark domain to protect the copyrighted style, surpassing traditional watermarking methods that distort the style characteristics. Specifically, we employ LLMs to extract condensed-lists utilizing the designed instance delimitation mechanism. These lists guide MiZero in generating the watermark. Extensive experiments demonstrate that MiZero effectively verifies text style copyright ownership against AI imitation.