  Recent works have theoretically and empirically shown that deep neural
networks (DNNs) have an inherent vulnerability to small perturbations. Applying
the Deep k-Nearest Neighbors (DkNN) classifier, we observe a dramatically
increasing robustness-accuracy trade-off as the layer goes deeper. In this
work, we propose a Deep Adversarially-Enhanced k-Nearest Neighbors (DAEkNN)
method which achieves higher robustness than DkNN and mitigates the
robustness-accuracy trade-off in deep layers through two key elements. First,
DAEkNN is based on an adversarially trained model. Second, DAEkNN makes
predictions by leveraging a weighted combination of benign and adversarial
training data. Empirically, we find that DAEkNN improves both the robustness
and the robustness-accuracy trade-off on MNIST and CIFAR-10 datasets.
