  The field of adversarial robustness has attracted significant attention in
machine learning. Contrary to the common approach of training models that are
accurate in average case, it aims at training models that are accurate for
worst case inputs, hence it yields more robust and reliable models. Put
differently, it tries to prevent an adversary from fooling a model. The study
of adversarial robustness is largely focused on $\ell_p-$bounded adversarial
perturbations, i.e. modifications of the inputs, bounded in some $\ell_p$ norm.
Nevertheless, it has been shown that state-of-the-art models are also
vulnerable to other more natural perturbations such as affine transformations,
which were already considered in machine learning within data augmentation.
This project reviews previous work in spatial robustness methods and proposes
evolution strategies as zeroth order optimization algorithms to find the worst
affine transforms for each input. The proposed method effectively yields robust
models and allows introducing non-parametric adversarial perturbations.
