Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security. By introducing perturbations to the training data, ULD degrades model performance, making it difficult for unauthorized models to extract useful representations. Despite the growing significance of ULD, existing surveys predominantly focus on related fields, such as adversarial attacks and machine unlearning, with little attention given to ULD as an independent area of study. This survey fills that gap by offering a comprehensive review of ULD, examining unlearnable data generation methods, public benchmarks, evaluation metrics, theoretical foundations and practical applications. We compare and contrast different ULD approaches, analyzing their strengths, limitations, and trade-offs related to unlearnability, imperceptibility, efficiency and robustness. Moreover, we discuss key challenges, such as balancing perturbation imperceptibility with model degradation and the computational complexity of ULD generation. Finally, we highlight promising future research directions to advance the effectiveness and applicability of ULD, underscoring its potential to become a crucial tool in the evolving landscape of data protection in machine learning.
Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学科学国家重点实验室，数学与系统科学研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；College of AI, Tsinghua University（清华大学人工智能学院）；Shanghai Qi Zhi Institute（上海启智研究院）
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Faculty of Engineering and Information Technology, The University of Melbourne（墨尔本大学工程与信息技术学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding
University of Electronic Science and Technology of China（电子科学与技术大学）；Independent Researcher（独立研究者）；University of Science and Technology of China（科学技术大学）；Nanyang Technological University（南洋理工大学）
Unlearnable Examples: Making Personal Data Unexploitable
The University of Melbourne（墨尔本大学）；Deakin University（迪肯大学）；Peking University（北京大学）
互联网上大量的“免费”数据一直是深度学习目前成功的关键。然而，它也引发了对未经授权利用个人数据来训练商业模型的隐私担忧。因此，至关重要的是要制定方法，以防止未经授权的数据利用。本文提出了一个问题：\emph{can data be made unlearnable for deep learning models?}我们提出了一种类型的\emph{error-minimizing}噪音，确实可以使训练样本不可学习。有意生成误差最小化噪声，以将一个或多个训练样本的误差减小到接近零，这可以欺骗模型，使其相信从这些样本中“没有什么”可以学习。噪声被限制为人眼无法察觉，因此不会影响正常的数据效用。我们经验验证的有效性，误差最小化的噪音在样本明智和类明智的形式。我们还展示了其灵活性下广泛的实验设置和实用性的人脸识别的案例研究。我们的工作为使个人数据无法被深度学习模型利用迈出了重要的第一步。
ARMOR: Shielding Unlearnable Examples against Data Augmentation
Nanyang Technological University（南洋理工大学）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络科学与工程学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center (HIC)（浙江大学杭州国际科技创新中心（HIC））；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
School of Computing；Augmented Intelligence, Arizona State University, Tempe, AZ, USA；Department of Computer Science；Engineering, Texas A\&M University, College Station, TX, USA
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
University of Illinois（伊利诺伊大学）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；National Center for Supercomputing Applications（国家超级计算应用中心）；Center for Astrophysical Surveys（天体物理巡天中心）
