  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
