  We propose an adversarial defense method that achieves state-of-the-art
performance among attack-agnostic adversarial defense methods while also
maintaining robustness to input resolution, scale of adversarial perturbation,
and scale of dataset size. Based on convolutional sparse coding, we construct a
stratified low-dimensional quasi-natural image space that faithfully
approximates the natural image space while also removing adversarial
perturbations. We introduce a novel Sparse Transformation Layer (STL) in
between the input image and the first layer of the neural network to
efficiently project images into our quasi-natural image space. Our experiments
show state-of-the-art performance of our method compared to other
attack-agnostic adversarial defense methods in various adversarial settings.
