Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as {\em Dynamic Time Warping for Adversarial Robustness (DTW-AR)} using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard Euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training. The source code of DTW-AR algorithms is available at https://github.com/tahabelkhouja/DTW-AR

翻译：尽管在深海神经网络(DNNs)的对抗性强健性研究上取得了迅速的进展,但在时间序列领域几乎没有原则性工作。由于时间序列数据出现在包括移动健康、金融和智能网格在内的各种应用中,因此,必须核实和改进时间序列域DNs的稳健性。在本文件中,我们提议了一个新框架,用于使用动态时间扭曲措施,对称为“反逆性强力动态时间转换”的时间序列域进行新的框架。提供了理论和经验证据,以证明DTW相对于先前图像域方法中使用的Euclidean距离标准标准值的有效性。我们开发了一种原则性算法,理论分析证明有理由使用随机校准路径有效地创建多种对抗性实例。关于不同现实世界基准的实验表明DTW-AR为时序数据作假,并利用对抗性训练提高DNs的稳性。DTW-AR算法的源代码见https://github.com/tahbabholikhoja/DTTW-AR-DTAR)