  Recent studies have highlighted adversarial examples as a ubiquitous threat
to different neural network models and many downstream applications.
Nonetheless, as unique data properties have inspired distinct and powerful
learning principles, this paper aims to explore their potentials towards
mitigating adversarial inputs. In particular, our results reveal the importance
of using the temporal dependency in audio data to gain discriminate power
against adversarial examples. Tested on the automatic speech recognition (ASR)
tasks and three recent audio adversarial attacks, we find that (i) input
transformation developed from image adversarial defense provides limited
robustness improvement and is subtle to advanced attacks; (ii) temporal
dependency can be exploited to gain discriminative power against audio
adversarial examples and is resistant to adaptive attacks considered in our
experiments. Our results not only show promising means of improving the
robustness of ASR systems, but also offer novel insights in exploiting
domain-specific data properties to mitigate negative effects of adversarial
examples.
