Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman,u00a0Alaa Khaddaj,u00a0Guillaume Leclerc,u00a0Andrew Ilyas,u00a0Aleksander Madry
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 practicalu2014one that involves the organizations developing diffusion models, rather than individual users, to implement (and support) the immunization process.
