Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

Researchers found a security weakness in fingerprinting methods for image generation AI models (systems that embed hidden identifiers into generated images to protect ownership). Existing fingerprinting techniques are vulnerable to collusion attacks, where multiple attackers combine their models to remove or hide these fingerprints. The researchers propose a new fingerprinting method that encodes fingerprints into a personalized normalization module (PNM, a customization layer in the model) and uses anti-collusion mechanisms based on mathematical transformations that severely degrade image quality if models are combined, making colluded models unusable.