Adversarial Detector with Robust Classifier
Takayuki Osakabe, AprilPyone MaungMaung, Sayaka Shiota, Hitoshi Kiya · 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) · 2022
Deep neural network (DNN) models are well-known 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.