Recent works on adversarial perturbations show that there is an inherent
trade-off between standard test accuracy and adversarial accuracy.
Specifically, they show that no classifier can simultaneously be robust to
adversarial perturbations and achieve high standard test accuracy. However,
this is contrary to the standard notion that on tasks such as image
classification, humans are robust classifiers with low error rate. In this
work, we show that the main reason behind this confusion is the inexact
definition of adversarial perturbation that is used in the literature. To fix
this issue, we propose a slight, yet important modification to the existing
definition of adversarial perturbation. Based on the modified definition, we
show that there is no trade-off between adversarial and standard accuracies;
there exist classifiers that are robust and achieve high standard accuracy. We
further study several properties of this new definition of adversarial risk and
its relation to the existing definition.

本文研究对抗扰动和定义的问题，提出对现有定义的轻微修改，消除了对抗精度和标准测试精度之间的权衡，并证明了在新定义下存在既鲁棒又获得高标准准确性的分类器。