  With the tremendous advances in the architecture and scale of convolutional
neural networks (CNNs) over the past few decades, they can easily reach or even
exceed the performance of humans in certain tasks. However, a recently
discovered shortcoming of CNNs is that they are vulnerable to adversarial
attacks. Although the adversarial robustness of CNNs can be improved by
adversarial training, there is a trade-off between standard accuracy and
adversarial robustness. From the neural architecture perspective, this paper
aims to improve the adversarial robustness of the backbone CNNs that have a
satisfactory accuracy. Under a minimal computational overhead, the introduction
of a dilation architecture is expected to be friendly with the standard
performance of the backbone CNN while pursuing adversarial robustness.
Theoretical analyses on the standard and adversarial error bounds naturally
motivate the proposed neural architecture dilation algorithm. Experimental
results on real-world datasets and benchmark neural networks demonstrate the
effectiveness of the proposed algorithm to balance the accuracy and adversarial
robustness.
