OmniMark: Efficient and Scalable Latent Diffusion Model Fingerprinting

Abstract

We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models (LDM). OmniMark can encode user-specific fingerprints across diverse dimensions of the weights of the LDM, including kernels, filters, channels, and spatial domains. The LDM is fine-tuned to encode the invisible fingerprint into generated images, which can be decoded by a decoder. By altering fingerprints and re-encoding the weights, OmniMark supports efficient and scalable ad-hoc generation (<100 ms) of numerous models with unique fingerprints that enable user accountability and model attribution. Extensive experiments demonstrate that OmniMark can be applied to various image generation and editing tasks and achieve highly accurate fingerprint detection without compromising image quality. Furthermore, OmniMark demonstrates good robustness against both white-box model attacks and image attacks, including fine-tuning and JPEG compression.