  Recent studies show that the deep neural networks (DNNs) have achieved great
success in various tasks. However, even the \emph{state-of-the-art} deep
learning based classifiers are extremely vulnerable to adversarial examples,
resulting in sharp decay of discrimination accuracy in the presence of enormous
unknown attacks. Given the fact that neural networks are widely used in the
open world scenario which can be safety-critical situations, mitigating the
adversarial effects of deep learning methods has become an urgent need.
Generally, conventional DNNs can be attacked with a dramatically high success
rate since their gradient is exposed thoroughly in the white-box scenario,
making it effortless to ruin a well trained classifier with only imperceptible
perturbations in the raw data space. For tackling this problem, we propose a
plug-and-play layer that is training-free, termed as \textbf{G}radient
\textbf{C}oncealment \textbf{M}odule (GCM), concealing the vulnerable direction
of gradient while guaranteeing the classification accuracy during the inference
time. GCM reports superior defense results on the ImageNet classification
benchmark, improving up to 63.41\% top-1 attack robustness (AR) when faced with
adversarial inputs compared to the vanilla DNNs. Moreover, we use GCM in the
CVPR 2022 Robust Classification Challenge, currently achieving \textbf{2nd}
place in Phase II with only a tiny version of ConvNext. The code will be made
available.
