  Deep convolutional neural networks (DCNNs) have dominated as the best
performers in machine learning, but can be challenged by adversarial attacks.
In this paper, we defend against adversarial attacks using neural architecture
search (NAS) which is based on a comprehensive search of denoising blocks,
weight-free operations, Gabor filters and convolutions. The resulting
anti-bandit NAS (ABanditNAS) incorporates a new operation evaluation measure
and search process based on the lower and upper confidence bounds (LCB and
UCB). Unlike the conventional bandit algorithm using UCB for evaluation only,
we use UCB to abandon arms for search efficiency and LCB for a fair competition
between arms. Extensive experiments demonstrate that ABanditNAS is faster than
other NAS methods, while achieving an $8.73\%$ improvement over prior arts on
CIFAR-10 under PGD-$7$.
