Optimization-Free Image Immunization Against Diffusion-Based Editing

• URL: http://arxiv.org/abs/2411.17957v1
• Date: Wed, 27 Nov 2024 00:30:26 GMT
• Title: Optimization-Free Image Immunization Against Diffusion-Based Editing
• Authors: Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali, James M. Rehg,
• Abstract summary: DiffVax is a scalable, lightweight, and optimization-free framework for image immunization. Our approach enables effective generalization to unseen content, reducing computational costs and cutting immunization time from days to milliseconds.
• Score: 23.787546784989484
• License:
• Abstract: Current image immunization defense techniques against diffusion-based editing embed imperceptible noise in target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming re-optimization for each image-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 250,000x speedup. 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 is provided in our project webpage.

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