  In this study, we propose the leveraging of interpretability for tasks beyond
purely the purpose of explainability. In particular, this study puts forward a
novel strategy for leveraging gradient-based interpretability in the realm of
adversarial examples, where we use insights gained to aid adversarial learning.
More specifically, we introduce the concept of spatially constrained one-pixel
adversarial perturbations, where we guide the learning of such adversarial
perturbations towards more susceptible areas identified via gradient-based
interpretability. Experimental results using different benchmark datasets show
that such a spatially constrained one-pixel adversarial perturbation strategy
can noticeably improve the speed of convergence as well as produce successful
attacks that were also visually difficult to perceive, thus illustrating an
effective use of interpretability methods for tasks outside of the purpose of
purely explainability.
