Deep Neural Networks (DNNs) have shown remarkable performance in a diverse
range of machine learning applications. However, it is widely known that DNNs
are vulnerable to simple adversarial perturbations, which causes the model to
incorrectly classify inputs. In this paper, we propose a simple yet effective
method to detect adversarial examples, using methods developed to explain the
model's behavior. Our key observation is that adding small, humanly
imperceptible perturbations can lead to drastic changes in the model
explanations, resulting in unusual or irregular forms of explanations. From
this insight, we propose an unsupervised detection of adversarial examples
using reconstructor networks trained only on model explanations of benign
examples. Our evaluations with MNIST handwritten dataset show that our method
is capable of detecting adversarial examples generated by the state-of-the-art
algorithms with high confidence. To the best of our knowledge, this work is the
first in suggesting unsupervised defense method using model explanations.

本文提出一种使用模型解释方法检测异常样例的无监督方法，并在 MNIST 数据集上展现了该方法高效检测最先进算法所产生的对手样本。