Exploring Robust Features for Improving Adversarial Robustness
This work addresses the fragility of DNNs in safety-critical applications, offering an incremental improvement by disentangling robust features to enhance robustness and maintain clean image accuracy.
The paper tackled the problem of deep neural networks' vulnerability to adversarial attacks by exploring robust features that are invariant to adversarial perturbations, resulting in improved adversarial robustness compared to state-of-the-art methods across four datasets and enabling adversarial example detection without extra computational cost.
While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applications. In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i.e., invariant to the clean image and its adversarial examples, to improve the model's adversarial robustness. Specifically, we propose a feature disentanglement model to segregate the robust features from non-robust features and domain specific features. The extensive experiments on four widely used datasets with different attacks demonstrate that robust features obtained from our model improve the model's adversarial robustness compared to the state-of-the-art approaches. Moreover, the trained domain discriminator is able to identify the domain specific features from the clean images and adversarial examples almost perfectly. This enables adversarial example detection without incurring additional computational costs. With that, we can also specify different classifiers for clean images and adversarial examples, thereby avoiding any drop in clean image accuracy.