As humans, we inherently perceive images based on their predominant features,
and ignore noise embedded within lower bit planes. On the contrary, Deep Neural
Networks are known to confidently misclassify images corrupted with
meticulously crafted perturbations that are nearly imperceptible to the human
eye. In this work, we attempt to address this problem by training networks to
form coarse impressions based on the information in higher bit planes, and use
the lower bit planes only to refine their prediction. We demonstrate that, by
imposing consistency on the representations learned across differently
quantized images, the adversarial robustness of networks improves significantly
when compared to a normally trained model. Present state-of-the-art defenses
against adversarial attacks require the networks to be explicitly trained using
adversarial samples that are computationally expensive to generate. While such
methods that use adversarial training continue to achieve the best results,
this work paves the way towards achieving robustness without having to
explicitly train on adversarial samples. The proposed approach is therefore
faster, and also closer to the natural learning process in humans.

通过针对不同的量化图像进行学习，以高位平面的信息为基础形成粗略的印象，并将低位平面仅用于细化预测，该方法与当前的对抗攻击防御方法相比，不用显式地对模型进行对抗性训练，从而实现了更快的训练速度和更接近自然学习过程的优点。