Exploring the differences in adversarial robustness between ViT- and CNN-based models using novel metrics

Deep-learning models have demonstrated remarkable performance in a variety of fields, owing to advancements in computational power and the availability of extensive datasets for training large-scale models. Nonetheless, these models inherently possess a vulnerability wherein even small alterations to the input can lead to substantially different outputs. Consequently, it is imperative to assess the robustness of deep-learning models prior to relying on their decision-making capabilities. In this study, we investigate the adversarial robustness of convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid CNNs ＋ViTs, which represent prevalent architectures in computer vision. Our evaluation is grounded on four novel model-sensitivity metrics that we introduce. These metrics are evaluated in the context of random noise and gradient-based adversarial perturbations. To ensure a fair comparison, we employ models with comparable capacities within each group and conduct experiments separately, utilizing ImageNet-1K and ImageNet-21K as pretraining data. Our fair experimental results provide empirical evidence that ViT-based models exhibit higher adversarial robustness than CNN-based counterparts, helping to dispel doubts about the findings of prior studies. Additionally, we introduce novel metrics that contribute new insights into the previously unconfirmed characteristics of these models.