Recently developed text-to-image diffusion models make it easy to edit or create high-quality images. Their ease of use has raised concerns about the potential for malicious editing or deepfake creation. Imperceptible perturbations have been proposed as a means of protecting images from malicious editing by preventing diffusion models from generating realistic images. However, we find that the aforementioned perturbations are not robust to JPEG compression, which poses a major weakness because of the common usage and availability of JPEG. We discuss the importance of robustness for additive imperceptible perturbations and encourage alternative approaches to protect images against editing.

翻译：近期开发的文本到图像扩散模型使得编辑或创建高质量图像变得简便。这种易用性引发了人们对恶意编辑或深度伪造制作的担忧。研究者已提出通过添加不可感知的扰动来保护图像免受恶意编辑，其原理是阻止扩散模型生成逼真图像。然而，我们发现上述扰动对JPEG压缩不具有鲁棒性——由于JPEG格式的普遍使用和易获取性，这一缺陷构成了重大隐患。本文探讨了不可感知加性扰动的鲁棒性关键问题，并倡导采用替代方案来保护图像免受编辑。