  Deep networks are vulnerable to adversarial examples. Adversarial Training
(AT) has been a standard foundation of modern adversarial defense approaches
due to its remarkable effectiveness. However, AT is extremely time-consuming,
refraining it from wide deployment in practical applications. In this paper, we
aim at a non-AT defense: How to design a defense method that gets rid of AT but
is still robust against strong adversarial attacks? To answer this question, we
resort to adaptive Batch Normalization (BN), inspired by the recent advances in
test-time domain adaptation. We propose a novel defense accordingly, referred
to as the Adaptive Batch Normalization Network (ABNN). ABNN employs a
pre-trained substitute model to generate clean BN statistics and sends them to
the target model. The target model is exclusively trained on clean data and
learns to align the substitute model's BN statistics. Experimental results show
that ABNN consistently improves adversarial robustness against both digital and
physically realizable attacks on both image and video datasets. Furthermore,
ABNN can achieve higher clean data performance and significantly lower training
time complexity compared to AT-based approaches.
