Toward Transferable Defense Against Malicious Image Edits

Diffusion-based image editing systems (AI tools that modify images based on text descriptions) can be manipulated maliciously, and while adding imperceptible perturbations (tiny, invisible changes) to images helps protect against this, existing defenses don't work well across different models. This paper proposes TDAE, a system that combines image and text-based defenses to create images that are harder to maliciously edit, even when attacked by unfamiliar editing models.