  Neural networks trained on visual data are well-known to be vulnerable to
often imperceptible adversarial perturbations. The reasons for this
vulnerability are still being debated in the literature. Recently Ilyas et al.
(2019) showed that this vulnerability arises, in part, because neural network
classifiers rely on highly predictive but brittle "non-robust" features. In
this paper we extend the work of Ilyas et al. by investigating the nature of
the input patterns that give rise to these features. In particular, we
hypothesize that in a neural network trained in a standard way, non-robust
features respond to small, "non-semantic" patterns that are typically entangled
with larger, robust patterns, known to be more human-interpretable, as opposed
to solely responding to statistical artifacts in a dataset. Thus, adversarial
examples can be formed via minimal perturbations to these small, entangled
patterns. In addition, we demonstrate a corollary of our hypothesis: robust
classifiers are more effective than standard (non-robust) ones as a source for
generating transferable adversarial examples in both the untargeted and
targeted settings. The results we present in this paper provide new insight
into the nature of the non-robust features responsible for adversarial
vulnerability of neural network classifiers.
