  In this paper, we introduce a novel technique based on the Secure Selective
Convolutional (SSC) techniques in the training loop that increases the
robustness of a given DNN by allowing it to learn the data distribution based
on the important edges in the input image. We validate our technique on
Convolutional DNNs against the state-of-the-art attacks from the open-source
Cleverhans library using the MNIST, the CIFAR-10, and the CIFAR-100 datasets.
Our experimental results show that the attack success rate, as well as the
imperceptibility of the adversarial images, can be significantly reduced by
adding effective pre-processing functions, i.e., Sobel filtering.
