BlurGuard：一种增强图像保护以抵御AI驱动编辑的简单方法
BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editing
AI总结：
针对图像保护对抗噪声易被JPEG压缩等简单技术逆转的问题，提出对噪声应用自适应逐区域高斯模糊以调整频谱的BlurGuard方法，有效提升了抵御多种逆转技术的鲁棒性并减少了图像质量退化。
摘要：
文本到图像模型的最新进展增加了强大图像编辑技术作为工具的曝光率，引发了人们对其潜在恶意用途的担忧。应对此类威胁的一条新兴研究路线是在图像公开发布前植入“保护性”对抗噪声，从而阻碍未来使用文本到图像模型对其进行编辑的尝试。然而，后续研究表明，这些对抗噪声往往很容易被“逆转”，例如使用JPEG压缩这样简单的技术，这使人们对该方法的实用性产生了怀疑。在本文中，我们认为用于图像保护的对抗噪声不仅应如先前工作的主要关注点那样是不可感知的，而且应是不可逆的，即在原始图像被隐藏的情况下，它应难以被检测为噪声。我们提出了一种出奇简单的方法来增强图像保护方法抵御噪声逆转技术的鲁棒性。具体而言，该方法对噪声应用自适应的逐区域高斯模糊来调整整体频谱。通过大量实验，我们表明，在多种图像编辑场景下，该方法持续提升了现有方法抵御广泛逆转技术的每样本最坏情况保护性能，同时减少了在感知指标上因噪声导致的图像质量退化。代码可在 https://github.com/jsu-kim/BlurGuard 获取。
英文摘要：
Recent advances in text-to-image models have increased the exposure of powerful image editing techniques as a tool, raising concerns about their potential for malicious use. An emerging line of research to address such threats focuses on implanting "protective" adversarial noise into images before their public release, so future attempts to edit them using text-to-image models can be impeded. However, subsequent works have shown that these adversarial noises are often easily "reversed," e.g., with techniques as simple as JPEG compression, casting doubt on the practicality of the approach. In this paper, we argue that adversarial noise for image protection should not only be imperceptible, as has been a primary focus of prior work, but also irreversible, viz., it should be difficult to detect as noise provided that the original image is hidden. We propose a surprisingly simple method to enhance the robustness of image protection methods against noise reversal techniques. Specifically, it applies an adaptive per-region Gaussian blur on the noise to adjust the overall frequency spectrum. Through extensive experiments, we show that our method consistently improves the per-sample worst-case protection performance of existing methods against a wide range of reversal techniques on diverse image editing scenarios, while also reducing quality degradation due to noise in terms of perceptual metrics. Code is available at https://github.com/jsu-kim/BlurGuard.
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。
