Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights. Researchers attempt to prevent malicious personalization using adversarial perturbations. However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations. They utilize global adversarial perturbations, which introduce noticeable alterations to original images and significantly degrade visual quality. In this work, we propose the Visual-Friendly Concept Protection (VCPro) framework, which prioritizes the protection of key concepts chosen by the image owner through adversarial perturbations with lower perceptibility. To ensure these perturbations are as inconspicuous as possible, we introduce a relaxed optimization objective to identify the least perceptible yet effective adversarial perturbations, solved using the Lagrangian multiplier method. Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.

翻译：通过少量图像微调扩散模型实现个性化概念生成，引发了关于隐私和知识产权方面的潜在法律与伦理问题。研究者尝试使用对抗扰动来防止恶意个性化。然而，先前的研究主要集中于保护的有效性，而忽视了扰动的可见性。它们采用全局对抗扰动，这会对原始图像引入明显的改变并显著降低视觉质量。在本工作中，我们提出了视觉友好型概念保护（VCPro）框架，该框架通过具有较低可感知性的对抗扰动，优先保护图像所有者选择的关键概念。为确保这些扰动尽可能不显眼，我们引入了一个宽松的优化目标，以识别最不易察觉且有效的对抗扰动，并使用拉格朗日乘子法进行求解。定性与定量实验验证了VCPro在扰动可见性与保护有效性之间取得了更好的平衡，能够以更不易察觉的扰动有效优先保护图像中的目标概念。