RANDOM MASK: Towards Robust Convolutional Neural Networks

Tiange Luo; Tianle Cai; Mengxiao Zhang; Siyu Chen; Liwei Wang

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arXiv · 2007.14249

RANDOM MASK: Towards Robust Convolutional Neural Networks

Tiange Luo·Tianle Cai·Mengxiao Zhang·Siyu Chen·Liwei Wang

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Abstract

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.

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BibTeX RIS

Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Liwei Wang. 2020-07-27. RANDOM MASK: Towards Robust Convolutional Neural Networks. https://arxiv.org/abs/2007.14249

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