基于语义注入的通用图像免疫方法抵御基于扩散模型的图像编辑
Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection
AI总结：
提出首个通用对抗扰动框架，通过语义注入使扩散模型误解输入图像，抑制原始内容，从而有效阻断未经授权的编辑，在通用扰动设置下优于基线，并具备黑盒迁移性。
摘要：
扩散模型的进步实现了强大的文本引导图像编辑，但也带来了深度伪造和未经授权使用等伦理和法律风险。为了防止这些风险，基于对抗攻击的图像免疫已成为抵御AI驱动语义操作的一种有前景的防御手段。然而，现有大多数方法需要针对特定图像进行优化或在推理时使用额外的神经网络，阻碍了可扩展性和实用性。在本文中，我们提出了首个基于通用对抗扰动的图像免疫框架，该框架生成一个单一的、与图像无关的对抗扰动，专门针对基于扩散模型的编辑流程。受目标攻击中使用的UAP启发，我们的方法旨在生成一个UAP，诱导扩散模型将输入图像误解为特定的语义目标。同时，它抑制原始内容以在编辑过程中误导模型的注意力，从而通过UAP覆盖图像的原始语义，有效阻止未经授权的编辑。大量实验表明，作为首个通用免疫方法，我们的方法在UAP设置下显著优于多个基线。值得注意的是，尽管通用扰动本身具有难度，但我们的方法在更严格的扰动预算下，与针对特定图像的方法相比，达到了竞争性或更优的性能，同时在多种扩散模型上展现出强大的黑盒迁移性。
英文摘要：
Diffusion model advances have enabled powerful text-guided image editing, but also raise ethical and legal risks such as deepfakes and unauthorized use. To prevent these risks, adversarial attack-based image immunization has emerged as a promising defense against AI-driven semantic manipulation. Yet, most existing approaches require image-specific optimization or additional neural networks at inference time, hindering scalability and practicality. In this paper, we propose the first universal adversarial perturbation-based image immunization framework that generates a single, image-agnostic adversarial perturbation specifically designed for diffusion-based editing pipelines. Inspired by UAP used in targeted attacks, our method aims to generate a UAP that induces diffusion models to misinterpret the input image as a specific semantic target. Simultaneously, it suppresses original content to misdirect the model's attention during editing, thereby effectively blocking unauthorized edits by overwriting the image's original semantics via the UAP. Extensive experiments show that our method, as the first universal immunization approach, significantly outperforms several baselines in the UAP setting. Notably, despite the inherent difficulty of universal perturbations, our method achieves competitive or superior performance compared to image-specific methods under a more restricted perturbation budget, while also exhibiting strong black-box transferability across diverse diffusion models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
