Watermarks vs. Perturbations for Preventing AI-Based Style Editing

Abstract

The remarkable image editing capabilities of generative models have led to growing concerns regarding unauthorized editing of multimedia. To mitigate against such misuse, artists and creators can utilize traditional image watermarking and more recent adversarial perturbation-based protection techniques to protect media assets. Watermarks generally protect the origin by establishing ownership, but can be easily removed. However, perturbation-based protection is aimed at disrupting editing and is harder to remove. In this paper, we evaluate the effectiveness of the two methods against Stable Diffusion in preventing the generation of usable edits.