Convolutional Neural Networks (CNNs) have become the de facto gold standard
in computer vision applications in the past years. Recently, however, new model
architectures have been proposed challenging the status quo. The Vision
Transformer (ViT) relies solely on attention modules, while the MLP-Mixer
architecture substitutes the self-attention modules with Multi-Layer
Perceptrons (MLPs). Despite their great success, CNNs have been widely known to
be vulnerable to adversarial attacks, causing serious concerns for
security-sensitive applications. Thus, it is critical for the community to know
whether the newly proposed ViT and MLP-Mixer are also vulnerable to adversarial
attacks. To this end, we empirically evaluate their adversarial robustness
under several adversarial attack setups and benchmark them against the widely
used CNNs. Overall, we find that the two architectures, especially ViT, are
more robust than their CNN models. Using a toy example, we also provide
empirical evidence that the lower adversarial robustness of CNNs can be
partially attributed to their shift-invariant property. Our frequency analysis
suggests that the most robust ViT architectures tend to rely more on
low-frequency features compared with CNNs. Additionally, we have an intriguing
finding that MLP-Mixer is extremely vulnerable to universal adversarial

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