  We focus on the robustness of neural networks for classification. To permit a
fair comparison between methods to achieve robustness, we first introduce a
standard based on the mensuration of a classifier's degradation. Then, we
propose natural perturbed training to robustify the network. Natural
perturbations will be encountered in practice: the difference of two images of
the same object may be approximated by an elastic deformation (when they have
slightly different viewing angles), by occlusions (when they hide differently
behind objects), or by saturation, Gaussian noise etc. Training some fraction
of the epochs on random versions of such variations will help the classifier to
learn better. We conduct extensive experiments on six datasets of varying sizes
and granularity. Natural perturbed learning show better and much faster
performance than adversarial training on clean, adversarial as well as natural
perturbed images. It even improves general robustness on perturbations not seen
during the training. For Cifar-10 and STL-10 natural perturbed training even
improves the accuracy for clean data and reaches the state of the art
performance. Ablation studies verify the effectiveness of natural perturbed
training.
