  Recent works reveal that re-calibrating the intermediate activation of
adversarial examples can improve the adversarial robustness of a CNN model. The
state of the arts [Baiet al., 2021] and [Yanet al., 2021] explores this feature
at the channel level, i.e. the activation of a channel is uniformly scaled by a
factor. In this paper, we investigate the intermediate activation manipulation
at a more fine-grained level. Instead of uniformly scaling the activation, we
individually adjust each element within an activation and thus propose
Element-Wise Activation Scaling, dubbed EWAS, to improve CNNs' adversarial
robustness. Experimental results on ResNet-18 and WideResNet with CIFAR10 and
SVHN show that EWAS significantly improves the robustness accuracy. Especially
for ResNet18 on CIFAR10, EWAS increases the adversarial accuracy by 37.65% to
82.35% against C&W attack. EWAS is simple yet very effective in terms of
improving robustness. The codes are anonymously available at
https://anonymous.4open.science/r/EWAS-DD64.
