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.

通过自适应批归一化网络（ABNN）设计一种不基于对抗训练但仍然能够抵御强对抗攻击的防御方法，以提高对抗性鲁棒性并在图像和视频数据集上实现更高的清洁数据性能和显著降低训练时间复杂度。

用于对抗鲁棒性的自适应批标准化网络

Adaptive Batch Normalization Networks for Adversarial Robustness

Bayesian Neural Networks (BNNs) have become one of the promising approaches
for uncertainty estimation due to the solid theorical foundations. However, the
performance of BNNs is affected by the ability of catching uncertainty. Instead
of only seeking the distribution of neural network weights by in-distribution
(ID) data, in this paper, we propose a new Bayesian Neural Network with an
Attached structure (ABNN) to catch more uncertainty from out-of-distribution
(OOD) data. We first construct a mathematical description for the uncertainty
of OOD data according to the prior distribution, and then develop an attached
Bayesian structure to integrate the uncertainty of OOD data into the backbone
network. ABNN is composed of an expectation module and several distribution
modules. The expectation module is a backbone deep network which focuses on the
original task, and the distribution modules are mini Bayesian structures which
serve as attachments of the backbone. In particular, the distribution modules
aim at extracting the uncertainty from both ID and OOD data. We further provide
theoretical analysis for the convergence of ABNN, and experimentally validate
its superiority by comparing with some state-of-the-art uncertainty estimation
methods Code will be made available.

基于贝叶斯神经网络的附加结构（ABNN）用于从新颖数据中提取不确定性，通过集成来自内部和外部数据的不确定性来改善性能，并通过理论分析和实验证实其优越性。