  With the increasing amount of available data and advances in computing
capabilities, deep neural networks (DNNs) have been successfully employed to
solve challenging tasks in various areas, including healthcare, climate, and
finance. Nevertheless, state-of-the-art DNNs are susceptible to
quasi-imperceptible perturbed versions of the original images -- adversarial
examples. These perturbations of the network input can lead to disastrous
implications in critical areas where wrong decisions can directly affect human
lives. Adversarial training is the most efficient solution to defend the
network against these malicious attacks. However, adversarial trained networks
generally come with lower clean accuracy and higher computational complexity.
This work proposes a data selection (DS) strategy to be applied in the
mini-batch training. Based on the cross-entropy loss, the most relevant samples
in the batch are selected to update the model parameters in the
backpropagation. The simulation results show that a good compromise can be
obtained regarding robustness and standard accuracy, whereas the computational
complexity of the backpropagation pass is reduced.
