MiZero: The Shadowy Defender Against Text Style Infringements
This addresses copyright protection for personal creative styles in text, which is a domain-specific issue, but the approach appears incremental as it builds on watermarking methods.
The paper tackles the problem of protecting text style copyrights against AI imitation by introducing MiZero, an implicit zero-watermarking scheme that establishes a watermark domain without distorting style characteristics, and experiments show it effectively verifies ownership.
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.