Dropping Pixels for Adversarial Robustness

Hossein Hosseini, Sreeram Kannan, Radha Poovendran; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Deep neural networks are vulnerable against adversarial examples. In this paper, we propose to train and test the networks with randomly subsampled images with high drop rates. We show that this approach significantly improves robustness against adversarial examples in all cases of bounded L0, L2 and L infinity perturbations, while reducing the standard accuracy by a small value. We argue that subsampling pixels can be thought to provide a set of robust features for the input image and, thus, improves robustness without performing adversarial training.

Related Material


[pdf]
[bibtex]
@InProceedings{Hosseini_2019_CVPR_Workshops,
author = {Hosseini, Hossein and Kannan, Sreeram and Poovendran, Radha},
title = {Dropping Pixels for Adversarial Robustness},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}