  Deep convolutional neural networks can be highly vulnerable to small
perturbations of their inputs, potentially a major issue or limitation on
system robustness when using deep networks as classifiers. In this paper we
propose a low-cost method to explore marginal sample data near trained
classifier decision boundaries, thus identifying potential adversarial samples.
By finding such adversarial samples it is possible to reduce the search space
of adversarial attack algorithms while keeping a reasonable successful
perturbation rate. In our developed strategy, the potential adversarial samples
represent only 61% of the test data, but in fact cover more than 82% of the
adversarial samples produced by iFGSM and 92% of the adversarial samples
successfully perturbed by DeepFool on CIFAR10.
