There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at \url{https://github.com/jiamingzhang94/Unlearnable-Clusters}.

翻译：针对互联网上的视觉隐私泄露问题，开发不可学习样本（UEs）日益引起关注。UEs是添加了不可见但不可学习噪声的训练样本，已被证实能够阻止机器学习模型未经授权的训练。UEs通常通过基于代理模型的双层优化框架生成，以消除（最小化）原始样本中的误差，进而用于保护数据免受未知目标模型的攻击。然而，现有UE生成方法均依赖于称为标签一致性的理想假设，即假定黑客与数据保护者对给定样本持有相同标签。本研究提出并推广了一种更为实际的标签无关场景：黑客可能以与保护者截然不同的方式利用受保护数据。例如，保护者持有的m类不可学习数据集，可能被黑客当作n类数据集使用。现有UE生成方法在此挑战性场景下均失效。为解决该问题，我们提出名为不可学习聚类（UCs）的新技术，通过聚类级扰动生成标签无关的不可学习样本。进一步，我们提出利用如CLIP等视觉-语言预训练模型（VLPMs）作为代理模型，以提升所构造UCs在不同领域的可迁移性。我们在包含不同数据集、不同目标模型乃至商业平台（微软Azure与百度飞桨）的多类场景下，通过实验验证了所提方法的有效性。代码已开源至\url{https://github.com/jiamingzhang94/Unlearnable-Clusters}。