We study the problem of defending deep neural network approaches for image
classification from physically realizable attacks. First, we demonstrate that
the two most scalable and effective methods for learning robust models,
adversarial training with PGD attacks and randomized smoothing, exhibit very
limited effectiveness against three of the highest profile physical attacks.
Next, we propose a new abstract adversarial model, rectangular occlusion
attacks, in which an adversary places a small adversarially crafted rectangle
in an image, and develop two approaches for efficiently computing the resulting
adversarial examples. Finally, we demonstrate that adversarial training using
our new attack yields image classification models that exhibit high robustness
against the physically realizable attacks we study, offering the first
effective generic defense against such attacks.

本篇研究论文主要探讨了如何有效防御基于图像分类的深度神经网络攻击。通过研究两种最常见的防御方法，我们发现这些方法对于三种最高危物理攻击的防御效果较差。因此我们提出了一种新的抽象对抗模型，矩形遮挡攻击，并且开发了两种计算结果的对抗样本的方法。最后，我们通过新的模型进行对抗训练，证明了这一方法是一种高效的通用防御策略。