Advances in Generative AI (GenAI) have led to the development of various protection strategies to prevent the unauthorized use of images. These methods rely on adding imperceptible protective perturbations to images to thwart misuse such as style mimicry or deepfake manipulations. Although previous attacks on these protections required specialized, purpose-built methods, we demonstrate that this is no longer necessary. We show that off-the-shelf image-to-image GenAI models can be repurposed as generic ``denoisers" using a simple text prompt, effectively removing a wide range of protective perturbations. Across 8 case studies spanning 6 diverse protection schemes, our general-purpose attack not only circumvents these defenses but also outperforms existing specialized attacks while preserving the image's utility for the adversary. Our findings reveal a critical and widespread vulnerability in the current landscape of image protection, indicating that many schemes provide a false sense of security. We stress the urgent need to develop robust defenses and establish that any future protection mechanism must be benchmarked against attacks from off-the-shelf GenAI models. Code is available in this repository: https://github.com/mlsecviswanath/img2imgdenoiser

翻译：生成式人工智能（GenAI）的进展催生了多种防止图像未经授权使用的保护策略。这些方法依赖于向图像添加难以察觉的保护性扰动，以阻止风格模仿或深度伪造篡改等滥用行为。尽管此前针对这些保护的攻击需要专门定制的方法，但我们证明这已不再必要。我们表明，使用简单的文本提示，现成的图像到图像生成式人工智能模型可被重新用作通用“去噪器”，有效消除多种保护性扰动。在涵盖6种不同保护方案的8个案例研究中，我们的通用攻击不仅成功规避了这些防御，而且在保持图像对攻击者可用性的同时，其性能超越了现有的专门攻击方法。我们的研究结果揭示了当前图像保护领域中一个关键且普遍存在的脆弱性，表明许多方案仅提供了虚假的安全感。我们强调迫切需要开发鲁棒的防御机制，并指出任何未来的保护方案都必须以现成生成式人工智能模型的攻击作为基准进行测试。代码已在此仓库中提供：https://github.com/mlsecviswanath/img2imgdenoiser