  Deep neural networks (DNNs) are well known to be vulnerable to adversarial
examples (AEs). In addition, AEs have adversarial transferability, which means
AEs generated for a source model can fool another black-box model (target
model) with a non-trivial probability. In this paper, we investigate the
property of adversarial transferability between models including ConvMixer,
which is an isotropic network, for the first time. To objectively verify the
property of transferability, the robustness of models is evaluated by using a
benchmark attack method called AutoAttack. In an image classification
experiment, ConvMixer is confirmed to be weak to adversarial transferability.
