Recently, text-to-image diffusion models have been widely used for style mimicry and personalized customization through methods such as DreamBooth and Textual Inversion. This has raised concerns about intellectual property protection and the generation of deceptive content. Recent studies, such as Glaze and Anti-DreamBooth, have proposed using adversarial noise to protect images from these attacks. However, recent purification-based methods, such as DiffPure and Noise Upscaling, have successfully attacked these latest defenses, showing the vulnerabilities of these methods. Moreover, present methods show limited transferability across models, making them less effective against unknown text-to-image models. To address these issues, we propose a novel anti-mimicry method, StyleGuard. We propose a novel style loss that optimizes the style-related features in the latent space to make it deviate from the original image, which improves model-agnostic transferability. Additionally, to enhance the perturbation's ability to bypass diffusion-based purification, we designed a novel upscale loss that involves ensemble purifiers and upscalers during training. Extensive experiments on the WikiArt and CelebA datasets demonstrate that StyleGuard outperforms existing methods in robustness against various transformations and purifications, effectively countering style mimicry in various models. Moreover, StyleGuard is effective on different style mimicry methods, including DreamBooth and Textual Inversion. The code is available at https://github.com/PolyLiYJ/StyleGuard.

翻译：近年来，文本到图像扩散模型已通过DreamBooth和Textual Inversion等方法被广泛用于风格模仿和个性化定制，这引发了关于知识产权保护和欺骗性内容生成的担忧。近期研究如Glaze和Anti-DreamBooth提出使用对抗性噪声来保护图像免受此类攻击。然而，基于纯化的最新方法（如DiffPure和Noise Upscaling）已成功攻破这些最新防御，揭示了这些方法的脆弱性。此外，现有方法在跨模型间的可迁移性有限，使其对未知的文本到图像模型效果不佳。为解决这些问题，我们提出了一种新颖的反模仿方法StyleGuard。我们设计了一种新颖的风格损失函数，通过优化潜在空间中与风格相关的特征，使其偏离原始图像，从而提升模型无关的可迁移性。同时，为增强扰动绕过基于扩散的纯化方法的能力，我们设计了一种新颖的上采样损失函数，在训练过程中集成纯化器和上采样器。在WikiArt和CelebA数据集上的大量实验表明，StyleGuard在对抗多种变换和纯化方法的鲁棒性上优于现有方法，能有效应对不同模型中的风格模仿。此外，StyleGuard对包括DreamBooth和Textual Inversion在内的多种风格模仿方法均有效。代码发布于https://github.com/PolyLiYJ/StyleGuard。