  Deep learning based models are vulnerable to adversarial attacks. These
attacks can be much more harmful in case of targeted attacks, where an attacker
tries not only to fool the deep learning model, but also to misguide the model
to predict a specific class. Such targeted and untargeted attacks are
specifically tailored for an individual sample and require addition of an
imperceptible noise to the sample. In contrast, universal adversarial attack
calculates a special imperceptible noise which can be added to any sample of
the given dataset so that, the deep learning model is forced to predict a wrong
class. To the best of our knowledge these targeted and universal attacks on
time series data have not been studied in any of the previous works. In this
work, we have performed untargeted, targeted and universal adversarial attacks
on UCR time series datasets. Our results show that deep learning based time
series classification models are vulnerable to these attacks. We also show that
universal adversarial attacks have good generalization property as it need only
a fraction of the training data. We have also performed adversarial training
based adversarial defense. Our results show that models trained adversarially
using Fast gradient sign method (FGSM), a single step attack, are able to
defend against FGSM as well as Basic iterative method (BIM), a popular
iterative attack.
