Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed perturbations which are imperceptible to humans but can cause the network to give incorrect outputs. In this paper, we design a new CNN architecture that by itself has good robustness. We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures. We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training. We next investigate the adversarial examples which 'fool' a CNN with Random Mask. Surprisingly, we find that these adversarial examples often 'fool' humans as well. This raises fundamental questions on how to define adversarial examples and robustness properly.
@article{arxiv.2007.14249,
title = {RANDOM MASK: Towards Robust Convolutional Neural Networks},
author = {Tiange Luo and Tianle Cai and Mengxiao Zhang and Siyu Chen and Liwei Wang},
journal= {arXiv preprint arXiv:2007.14249},
year = {2020}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1911.08432