  We investigate to what extent alternative variants of Artificial Neural
Networks (ANNs) are susceptible to adversarial attacks. We analyse the
adversarial robustness of conventional, stochastic ANNs and Spiking Neural
Networks (SNNs) in the raw image space, across three different datasets. Our
experiments reveal that stochastic ANN variants are almost equally as
susceptible as conventional ANNs when faced with simple iterative
gradient-based attacks in the white-box setting. However we observe, that in
black-box settings, stochastic ANNs are more robust than conventional ANNs,
when faced with boundary attacks, transferability and surrogate attacks.
Consequently, we propose improved attacks and defence mechanisms for stochastic
ANNs in black-box settings. When performing surrogate-based black-box attacks,
one can employ stochastic models as surrogates to observe higher attack success
on both stochastic and deterministic targets. This success can be further
improved with our proposed Variance Mimicking (VM) surrogate training method,
against stochastic targets. Finally, adopting a defender's perspective, we
investigate the plausibility of employing stochastic switching of model
mixtures as a viable hardening mechanism. We observe that such a scheme does
provide a partial hardening.
