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）
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James Bailey
Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing noises that can fool a DNN model into believing that there is nothing (no error) to learn from the data. The concept of UE has been proposed as a countermeasure against unauthorized data exploitation on personal data. While UE has been extensively studied on images, it is unclear how to craft effective UEs for time series data. In this work, we introduce the first UE generation method to protect time series data from unauthorized training by deep learning models. To this end, we propose a new form of error-minimizing noise that can be \emph{selectively} applied to specific segments of time series, rendering them unlearnable to DNN models while remaining imperceptible to human observers. Through extensive experiments on a wide range of time series datasets, we demonstrate that the proposed UE generation method is effective in both classification and generation tasks. It can protect time series data against unauthorized exploitation, while preserving their utility for legitimate usage, thereby contributing to the development of secure and trustworthy machine learning systems.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）
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）
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）
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
