Diffusion-based image generation models, such as Stable Diffusion or DALL-E 2, are able to learn from given images and generate high-quality samples following the guidance from prompts. For instance, they can be used to create artistic images that mimic the style of an artist based on his/her original artworks or to maliciously edit the original images for fake content. However, such ability also brings serious ethical issues without proper authorization from the owner of the original images. In response, several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations, which are designed to mislead the diffusion model and make it unable to properly generate new samples. In this work, we introduce a perturbation purification platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure. IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e.g., style mimicking, malicious editing). The proposed IMPRESS platform offers a comprehensive evaluation of several contemporary protection methods, and can be used as an evaluation platform for future protection methods.

翻译：扩散式图像生成模型（例如Stable Diffusion或DALL-E 2）能够从给定图像中学习，并根据提示引导生成高质量样本。例如，它们可用于根据艺术家的原创作品模仿其风格创作艺术图像，或恶意编辑原始图像以生成虚假内容。然而，这种能力在未获得原始图像所有者适当授权的情况下，也带来了严重的伦理问题。为此，研究者尝试通过在原始图像中添加旨在误导扩散模型、使其无法正常生成新样本的不可察觉扰动，来保护图像免受此类未经授权的数据使用。本文提出一个名为IMPRESS的扰动净化平台，用于评估不可察觉扰动作为保护措施的有效性。IMPRESS基于关键观察：不可察觉扰动可能导致原始图像与扩散重建图像之间出现可察觉的不一致性。利用这一现象，可设计一种新的优化策略对图像进行净化，从而可能削弱原始图像免受未经授权数据使用（例如风格模仿、恶意编辑）的保护效果。所提出的IMPRESS平台能够对多种当代保护方法进行全面评估，并可作为未来保护方法的评估平台。