Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection

Diffusion model advances have enabled powerful text-guidedimage editing, but also raise ethical and legal risks such as deepfakesand unauthorized use. To prevent these risks, adversarial attack-basedimage immunization has emerged as a promising defense against AI-driven semantic manipulation. Yet, most existing approaches requireimage-specific optimization or additional neural networks at inferencetime, hindering scalability and practicality. In this paper, we proposethe first universal adversarial perturbation-based image immunizationframework that generates a single, image-agnostic adversarial perturbationspecifically designed for diffusion-based editing pipelines. Inspired by UAPused in targeted attacks, our method aims to generate a UAP that inducesdiffusion models to misinterpret the input image as a specific semantictarget. Simultaneously, it suppresses original content to misdirect themodel’s attention during editing, thereby effectively blocking unauthorizededits by overwriting the image’s original semantics via the UAP. Extensiveexperiments show that our method, as the first universal immunizationapproach, significantly outperforms several baselines in the UAP setting.Notably, despite the inherent difficulty of universal perturbations, ourmethod achieves competitive or superior performance compared to image-specific methods under a more restricted perturbation budget, while alsoexhibiting strong black-box transferability across diverse diffusion models.