  Deep neural networks have been shown to be vulnerable to adversarial
examples: very small perturbations of the input having a dramatic impact on the
predictions. A wealth of adversarial attacks and distance metrics to quantify
the similarity between natural and adversarial images have been proposed,
recently enlarging the scope of adversarial examples with geometric
transformations beyond pixel-wise attacks. In this context, we investigate the
robustness to adversarial attacks of new Convolutional Neural Network
architectures providing equivariance to rotations. We found that
rotation-equivariant networks are significantly less vulnerable to
geometric-based attacks than regular networks on the MNIST, CIFAR-10, and
ImageNet datasets.
