  We present an approach to mitigating the risks of malicious image editing
posed by large diffusion models. The key idea is to immunize images so as to
make them resistant to manipulation by these models. This immunization relies
on injection of imperceptible adversarial perturbations designed to disrupt the
operation of the targeted diffusion models, forcing them to generate
unrealistic images. We provide two methods for crafting such perturbations, and
then demonstrate their efficacy. Finally, we discuss a policy component
necessary to make our approach fully effective and practical -- one that
involves the organizations developing diffusion models, rather than individual
users, to implement (and support) the immunization process.
