  Despite multiple efforts made towards robust machine learning (ML) models,
their vulnerability to adversarial examples remains a challenging problem that
calls for rethinking the defense strategy. In this paper, we take a step back
and investigate the causes behind ML models' susceptibility to adversarial
examples. In particular, we focus on exploring the cause-effect link between
adversarial examples and the out-of-distribution (OOD) problem. To that end, we
propose an OOD generalization method that stands against both adversary-induced
and natural distribution shifts. Through an OOD to in-distribution mapping
intuition, our approach translates OOD inputs to the data distribution used to
train and test the model. Through extensive experiments on three benchmark
image datasets of different scales (MNIST, CIFAR10, and ImageNet) and by
leveraging image-to-image translation methods, we confirm that the adversarial
examples problem is a special case of the wider OOD generalization problem.
Across all datasets, we show that our translation-based approach consistently
improves robustness to OOD adversarial inputs and outperforms state-of-the-art
defenses by a significant margin, while preserving the exact accuracy on benign
(in-distribution) data. Furthermore, our method generalizes on naturally OOD
inputs such as darker or sharper images
