  Deep Neural Networks (DNNs) are being used to solve a wide range of problems
in many domains including safety-critical domains like self-driving cars and
medical imagery. DNNs suffer from vulnerability against adversarial attacks. In
the past few years, numerous approaches have been proposed to tackle this
problem by training networks using adversarial training. Almost all the
approaches generate adversarial examples for the entire training dataset, thus
increasing the training time drastically. We show that we can decrease the
training time for any adversarial training algorithm by using only a subset of
training data for adversarial training. To select the subset, we filter the
adversarially-prone samples from the training data. We perform a simple
adversarial attack on all training examples to filter this subset. In this
attack, we add a small perturbation to each pixel and a few grid lines to the
input image.
  We perform adversarial training on the adversarially-prone subset and mix it
with vanilla training performed on the entire dataset. Our results show that
when our method-agnostic approach is plugged into FGSM, we achieve a speedup of
3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust
accuracy. We also test our approach on state-of-the-art Free adversarial
training and achieve a speedup of 1.2x in training time with a marginal drop in
robust accuracy on the ImageNet dataset.
