  Deep neural network (DNN) models are wellknown to easily misclassify
prediction results by using input images with small perturbations, called
adversarial examples. In this paper, we propose a novel adversarial detector,
which consists of a robust classifier and a plain one, to highly detect
adversarial examples. The proposed adversarial detector is carried out in
accordance with the logits of plain and robust classifiers. In an experiment,
the proposed detector is demonstrated to outperform a state-of-the-art detector
without any robust classifier.
