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.

提出了第一种针对时间序列数据的非法训练的UE生成方法，通过引入一种新形式的误差最小化噪声，可以选择性地应用于时间序列的特定片段，使其对DNN模型无法学习，同时对人类观察者不可察觉。通过对多个时间序列数据集进行广泛实验证明，该UE生成方法在分类和生成任务中都非常有效，可以保护时间序列数据免受未授权利用，并在合法使用中保持其实用性，从而为安全可靠的机器学习系统的发展做出贡献。

时间序列的无法学习示例