  Despite numerous attempts to defend deep learning based image classifiers,
they remain susceptible to the adversarial attacks. This paper proposes a
technique to identify susceptible classes, those classes that are more easily
subverted. To identify the susceptible classes we use distance-based measures
and apply them on a trained model. Based on the distance among original
classes, we create mapping among original classes and adversarial classes that
helps to reduce the randomness of a model to a significant amount in an
adversarial setting. We analyze the high dimensional geometry among the feature
classes and identify the k most susceptible target classes in an adversarial
attack. We conduct experiments using MNIST, Fashion MNIST, CIFAR-10 (ImageNet
and ResNet-32) datasets. Finally, we evaluate our techniques in order to
determine which distance-based measure works best and how the randomness of a
model changes with perturbation.
