Title: DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: diffusion, malicious editing, immunization, defense against editing

Abstract: Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming re-optimization for each image separately, taking hours for small batches. To address these challenges, we introduce DiffVax, a scalable, lightweight, and optimization-free framework for image immunization, specifically designed to prevent diffusion-based editing. Our approach enables effective generalization to unseen content, reducing computational costs and cutting immunization time from days to milliseconds, achieving a speedup of 250,000×. This is achieved through a loss term that ensures the failure of editing attempts and the imperceptibility of the perturbations. Extensive qualitative and quantitative results demonstrate that our model is scalable, optimization-free, adaptable to various diffusion-based editing tools, robust against counter-attacks, and, for the first time, effectively protects video content from editing. Our code and qualitative results are provided in the supplementary.

TL;DR: We introduce DiffVax, an end-to-end framework for training an "immunizer model" that learns how to generate imperceptible perturbations to immunize target images against diffusion-based editing.