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 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 250,000x. 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. More details are available in https://diffvax.github.io/ .
@article{arxiv.2411.17957,
title = {DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing},
author = {Tarik Can Ozden and Ozgur Kara and Oguzhan Akcin and Kerem Zaman and Shashank Srivastava and Sandeep P. Chinchali and James M. Rehg},
journal= {arXiv preprint arXiv:2411.17957},
year = {2026}
}
Comments
Accepted into ICLR 2026. Project webpage: https://diffvax.github.io/