Is Perturbation-Based Image Protection Disruptive to Image Editing?

2025-06-04

AI Summary

This paper systematically evaluates the effectiveness of perturbation-based image protection methods against text-guided editing in diffusion models. Addressing the central question—“Can imperceptible noise perturbations impede text-driven image editing?”—we conduct empirical studies across multiple tasks (e.g., image-to-image translation, style transfer) and domains (natural scenes and artistic images) on mainstream diffusion models, including Stable Diffusion. Contrary to conventional assumptions, our results demonstrate that existing perturbation methods fail to provide robust protection; instead, they consistently improve editing fidelity and prompt alignment. We further uncover a previously unrecognized phenomenon: perturbations strengthen implicit semantic alignment between protected images and textual prompts—a counterintuitive “protection paradox.” This finding fundamentally challenges the prevailing protection paradigm grounded solely in perceptual invisibility. Our work provides critical theoretical insights and practical implications for designing robust image copyright protection mechanisms in the era of generative AI.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Data transparency and provenance

Abstract

The remarkable image generation capabilities of state-of-the-art diffusion models, such as Stable Diffusion, can also be misused to spread misinformation and plagiarize copyrighted materials. To mitigate the potential risks associated with image editing, current image protection methods rely on adding imperceptible perturbations to images to obstruct diffusion-based editing. A fully successful protection for an image implies that the output of editing attempts is an undesirable, noisy image which is completely unrelated to the reference image. In our experiments with various perturbation-based image protection methods across multiple domains (natural scene images and artworks) and editing tasks (image-to-image generation and style editing), we discover that such protection does not achieve this goal completely. In most scenarios, diffusion-based editing of protected images generates a desirable output image which adheres precisely to the guidance prompt. Our findings suggest that adding noise to images may paradoxically increase their association with given text prompts during the generation process, leading to unintended consequences such as better resultant edits. Hence, we argue that perturbation-based methods may not provide a sufficient solution for robust image protection against diffusion-based editing.

Problem

• Evaluating effectiveness of perturbation-based image protection against diffusion editing
• Testing if protected images resist editing while maintaining desired output quality
• Assessing unintended consequences of noise on text-prompt adherence during generation

Innovation

• Perturbation-based image protection disrupts editing
• Noise enhances image-text association during generation
• Current methods insufficient against diffusion editing