  Deep Neural Networks are known to be vulnerable to small, adversarially
crafted, perturbations. The current most effective defense methods against
these adversarial attacks are variants of adversarial training. In this paper,
we introduce a radically different defense trained only on clean images: a
sparse coding based frontend which significantly attenuates adversarial attacks
before they reach the classifier. We evaluate our defense on CIFAR-10 dataset
under a wide range of attack types (including Linf , L2, and L1 bounded
attacks), demonstrating its promise as a general-purpose approach for defense.
