  Time-series data arises in many real-world applications (e.g., mobile health)
and deep neural networks (DNNs) have shown great success in solving them.
Despite their success, little is known about their robustness to adversarial
attacks. In this paper, we propose a novel adversarial framework referred to as
Time-Series Attacks via STATistical Features (TSA-STAT)}. To address the unique
challenges of time-series domain, TSA-STAT employs constraints on statistical
features of the time-series data to construct adversarial examples. Optimized
polynomial transformations are used to create attacks that are more effective
(in terms of successfully fooling DNNs) than those based on additive
perturbations. We also provide certified bounds on the norm of the statistical
features for constructing adversarial examples. Our experiments on diverse
real-world benchmark datasets show the effectiveness of TSA-STAT in fooling
DNNs for time-series domain and in improving their robustness. The source code
of TSA-STAT algorithms is available at
https://github.com/tahabelkhouja/Time-Series-Attacks-via-STATistical-Features
