Fingerprinting in EEG Model IP Protection Using Diffusion Model

In the rapidly advancing field of deep learning, a significant yet often overlooked challenge is the protection of intellectual property (IP) for models based on electroencephalography (EEG). These models, which handle sensitive and private physiological information, have not received as much attention for IP protection as their counterparts in more mainstream areas like computer vision (CV) and natural language processing (NLP). This paper introduces an innovative fingerprinting method for the first time, targeting IP protection of EEG-based models, a domain where conventional watermarking techniques fall short. We design a novel conditional diffusion model, tailored to a universal EEG format, which is the first application of diffusion models in model IP protection. Furthermore, our retrieval strategy, characterized by three distinct conditions, facilitates the construction of the fingerprint validation set from synthesized EEG samples. Experiments demonstrate that our method not only outperforms existing state-of-the-art (SOTA) protection techniques in robustness against various IP attacks but also excels in generating high-quality and high-diversity EEG samples.