  Deep neural networks are vulnerable to adversarial examples, i.e.,
carefully-perturbed inputs aimed to mislead classification. This work proposes
a detection method based on combining non-linear dimensionality reduction and
density estimation techniques. Our empirical findings show that the proposed
approach is able to effectively detect adversarial examples crafted by
non-adaptive attackers, i.e., not specifically tuned to bypass the detection
method. Given our promising results, we plan to extend our analysis to adaptive
attackers in future work.
