  Classifiers trained using conventional empirical risk minimization or maximum
likelihood methods are known to suffer dramatic performance degradations when
tested over examples adversarially selected based on knowledge of the
classifier's decision rule. Due to the prominence of Artificial Neural Networks
(ANNs) as classifiers, their sensitivity to adversarial examples, as well as
robust training schemes, have been recently the subject of intense
investigation. In this paper, for the first time, the sensitivity of spiking
neural networks (SNNs), or third-generation neural networks, to adversarial
examples is studied. The study considers rate and time encoding, as well as
rate and first-to-spike decoding. Furthermore, a robust training mechanism is
proposed that is demonstrated to enhance the performance of SNNs under
white-box attacks.
