Adversarial examples, generated by adding small but intentionally
imperceptible perturbations to normal examples, can mislead deep neural
networks (DNNs) to make incorrect predictions. Although much work has been done
on both adversarial attack and defense, a fine-grained understanding of
adversarial examples is still lacking. To address this issue, we present a
visual analysis method to explain why adversarial examples are misclassified.
The key is to compare and analyze the datapaths of both the adversarial and
normal examples. A datapath is a group of critical neurons along with their
connections. We formulate the datapath extraction as a subset selection problem
and solve it by constructing and training a neural network. A multi-level
visualization consisting of a network-level visualization of data flows, a
layer-level visualization of feature maps, and a neuron-level visualization of
learned features, has been designed to help investigate how datapaths of
adversarial and normal examples diverge and merge in the prediction process. A
quantitative evaluation and a case study were conducted to demonstrate the
promise of our method to explain the misclassification of adversarial examples.

本文提出一种视觉分析方法，通过比较和分析对抗和正常示例的数据路径，解释为什么对抗性示例被错误分类。使用神经网络构建和训练解决其子集选择问题，采用多层可视化来帮助研究数据路径的。