  We identify fragile and robust neurons of deep learning architectures using
nodal dropouts of the first convolutional layer. Using an adversarial targeting
algorithm, we correlate these neurons with the distribution of adversarial
attacks on the network. Adversarial robustness of neural networks has gained
significant attention in recent times and highlights intrinsic weaknesses of
deep learning networks against carefully constructed distortion applied to
input images. In this paper, we evaluate the robustness of state-of-the-art
image classification models trained on the MNIST and CIFAR10 datasets against
the fast gradient sign method attack, a simple yet effective method of
deceiving neural networks. Our method identifies the specific neurons of a
network that are most affected by the adversarial attack being applied. We,
therefore, propose to make fragile neurons more robust against these attacks by
compressing features within robust neurons and amplifying the fragile neurons
proportionally.
