Dropping Pixels for Adversarial Robustness

@article{Hosseini2019DroppingPF,
  title={Dropping Pixels for Adversarial Robustness},
  author={Hossein Hosseini and Sreeram Kannan and Radha Poovendran},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2019},
  pages={91-97}
}
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∞ 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… 

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