  Deep neural networks (DNNs) have played a key role in a wide range of machine
learning applications. However, DNN classifiers are vulnerable to
human-imperceptible adversarial perturbations, which can cause them to
misclassify inputs with high confidence. Thus, creating robust DNNs which can
defend against malicious examples is critical in applications where security
plays a major role. In this paper, we study the effect of intrinsic dataset
properties on the performance of adversarial attack and defense methods,
testing on five popular image classification datasets - MNIST, Fashion-MNIST,
CIFAR10/CIFAR100, and ImageNet. We find that input size and image contrast play
key roles in attack and defense success. Our discoveries highlight that dataset
design and data preprocessing steps are important to boost the adversarial
robustness of DNNs. To our best knowledge, this is the first comprehensive work
that studies the effect of intrinsic dataset properties on adversarial machine
learning.
