On the interplay of adversarial robustness and architecture components: patches, convolution and attention

09/14/2022
by   Francesco Croce, et al.
0

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 ℓ_∞-robustness.

READ FULL TEXT

page 4

page 5

page 9

page 10

page 11

10/14/2022

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Vision Transformers (ViTs) have recently achieved competitive performanc...
07/25/2023

On the unreasonable vulnerability of transformers for image restoration – and an easy fix

Following their success in visual recognition tasks, Vision Transformers...
11/25/2021

Clustering Effect of (Linearized) Adversarial Robust Models

Adversarial robustness has received increasing attention along with the ...
03/03/2023

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

While adversarial training has been extensively studied for ResNet archi...
09/28/2022

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effecti...
07/05/2018

Explainable Learning: Implicit Generative Modelling during Training for Adversarial Robustness

We introduce Explainable Learning ,ExL, an approach for training neural ...
02/11/2020

Adversarial Robustness for Code

We propose a novel technique which addresses the challenge of learning a...

Please sign up or login with your details

Forgot password? Click here to reset