Adversarial Detector with Robust Classifier

• URL: http://arxiv.org/abs/2202.02503v1
• Date: Sat, 5 Feb 2022 07:21:05 GMT
• Title: Adversarial Detector with Robust Classifier
• Authors: Takayuki Osakabe and Maungmaung Aprilpyone and Sayaka Shiota and Hitoshi Kiya
• Abstract summary: We propose a novel adversarial detector, which consists of a robust classifier and a plain one, to highly detect adversarial examples. In an experiment, the proposed detector is demonstrated to outperform a state-of-the-art detector without any robust classifier.
• Score: 14.586106862913553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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