Statistical Verification of General Perturbations by Gaussian Smoothing

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • TL;DR: We present a statistical certification method to certify robustness for rotations, translations and other transformations.
  • Abstract: We present a novel statistical certification method that generalizes prior work based on smoothing to handle richer perturbations. Concretely, our method produces a provable classifier which can establish statistical robustness against geometric perturbations (e.g., rotations, translations) as well as volume changes and pitch shifts on audio data. The generalization is non-trivial and requires careful handling of operations such as interpolation. Our method is agnostic to the choice of classifier and scales to modern architectures such as ResNet-50 on ImageNet.
  • Keywords: adversarial robustness, certified network, randomised smoothing, geometric perturbations
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