  The robustness testing has been performed for standard CNN models and Vision
Transformers, however there is a lack of comprehensive study between the
robustness of traditional Vision Transformers without an extra attentional
channel design and the latest fully attentional network(FAN) models. So in this
paper, we use the ImageNet dataset to compare the robustness of fully
attentional network(FAN) models with traditional Vision Transformers to
understand the role of an attentional channel processing design using white box
attacks and also study the transferability between the same using black box
attacks.
