  The adversarial vulnerability of deep networks has spurred the interest of
researchers worldwide. Unsurprisingly, like images, adversarial examples also
translate to time-series data as they are an inherent weakness of the model
itself rather than the modality. Several attempts have been made to defend
against these adversarial attacks, particularly for the visual modality. In
this paper, we perform detailed benchmarking of well-proven adversarial defense
methodologies on time-series data. We restrict ourselves to the $L_{\infty}$
threat model. We also explore the trade-off between smoothness and clean
accuracy for regularization-based defenses to better understand the trade-offs
that they offer. Our analysis shows that the explored adversarial defenses
offer robustness against both strong white-box as well as black-box attacks.
This paves the way for future research in the direction of adversarial attacks
and defenses, particularly for time-series data.
