On the interplay of adversarial robustness and architecture components:
patches, convolution and attention
- URL: http://arxiv.org/abs/2209.06953v1
- Date: Wed, 14 Sep 2022 22:02:32 GMT
- Title: On the interplay of adversarial robustness and architecture components:
patches, convolution and attention
- Authors: Francesco Croce, Matthias Hein
- Abstract summary: We study the effect of adversarial training on the interpretability of the learnt features and robustness to unseen threat models.
An ablation from ResNet to ConvNeXt reveals key architectural changes leading to almost $10%$ higher $ell_infty$-robustness.
- Score: 65.20660287833537
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years novel architecture components for image classification have
been developed, starting with attention and patches used in transformers. While
prior works have analyzed the influence of some aspects of architecture
components on the robustness to adversarial attacks, in particular for vision
transformers, the understanding of the main factors is still limited. We
compare several (non)-robust classifiers with different architectures and study
their properties, including the effect of adversarial training on the
interpretability of the learnt features and robustness to unseen threat models.
An ablation from ResNet to ConvNeXt reveals key architectural changes leading
to almost $10\%$ higher $\ell_\infty$-robustness.
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