论文标题
具有强大分类器的对抗检测器
Adversarial Detector with Robust Classifier
论文作者
论文摘要
深度神经网络（DNN）模型是众所周知的，可以通过使用带有小扰动的输入图像（称为对抗性示例）来容易地错误地分类预测结果。在本文中，我们提出了一个新型的对抗探测器，该检测器由强大的分类器和普通分类器组成，以高度检测对抗性例子。拟议的对抗探测器是根据普通和强大的分类器的逻辑进行的。在一个实验中，证明了所提出的检测器可以超越最先进的检测器，而没有任何可靠的分类器。
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.
