  Deep Neural Networks (DNNs) have been shown to be prone to adversarial
attacks. Memristive crossbars, being able to perform
Matrix-Vector-Multiplications (MVMs) efficiently, are used to realize DNNs on
hardware. However, crossbar non-idealities have always been devalued since they
cause errors in performing MVMs, leading to computational accuracy losses in
DNNs. Several software-based defenses have been proposed to make DNNs
adversarially robust. However, no previous work has demonstrated the advantage
conferred by the crossbar non-idealities in unleashing adversarial robustness.
We show that the intrinsic hardware non-idealities yield adversarial robustness
to the mapped DNNs without any additional optimization. We evaluate the
adversarial resilience of state-of-the-art DNNs (VGG8 & VGG16 networks) using
benchmark datasets (CIFAR-10, CIFAR-100 & Tiny Imagenet) across various
crossbar sizes. We find that crossbar non-idealities unleash significantly
greater adversarial robustness (>10-20%) in crossbar-mapped DNNs than baseline
software DNNs. We further assess the performance of our approach with other
state-of-the-art efficiency-driven adversarial defenses and find that our
approach performs significantly well in terms of reducing adversarial loss.
