标题： 基于图的方法结合特定分布距离的对抗攻击检测 显示英文标题

标题： Graph-based methods coupled with specific distributional distances for adversarial attack detection

摘要： 人工神经网络容易被经过精心扰动的输入所欺骗，这些输入会导致严重的错误分类。 这些\textit{对抗的}攻击一直是大量研究的重点。 同样，关于检测和防御这些攻击的方法也有大量研究。 我们从图的角度引入了一种新的检测和解释对抗攻击的方法。 对于输入图像，我们使用逐层相关传播算法\cite{bach15}计算一个相关的稀疏图。 具体来说，我们只保留神经网络中相关性值最高的边。 然后从图中计算出三个量，并将其与训练集计算出的值进行比较。 比较的结果是将图像分类为良性或对抗性的。 为了进行比较，介绍了两种分类方法：1）基于Wasserstein距离的显式公式，应用于节点的度数，2）逻辑回归。 这两种分类方法都产生了良好的结果，使我们相信基于图的对抗攻击解释是有价值的。 显示英文摘要

摘要： Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These \textit{adversarial} attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them. We introduce a novel approach of detection and interpretation of adversarial attacks from a graph perspective. For an input image, we compute an associated sparse graph using the layer-wise relevance propagation algorithm \cite{bach15}. Specifically, we only keep edges of the neural network with the highest relevance values. Three quantities are then computed from the graph which are then compared against those computed from the training set. The result of the comparison is a classification of the image as benign or adversarial. To make the comparison, two classification methods are introduced: 1) an explicit formula based on Wasserstein distance applied to the degree of node and 2) a logistic regression. Both classification methods produce strong results which lead us to believe that a graph-based interpretation of adversarial attacks is valuable.