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ADef: an Iterative Algorithm to Construct Adversarial Deformations

by R. Alaifari and G.S. Alberti and T. Gauksson

(Report number 2018-15)

Abstract
While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with a convolutional neural network and on ImageNet with Inception-v3 and ResNet-101.

Keywords: Adversarial Examples, Adversarial Deformations, Deep Learning, Neural Networks, Machine Learning, Stability

@Techreport{AAG18_769,
  author = {R. Alaifari and G.S. Alberti and T. Gauksson},
  title = {ADef: an Iterative Algorithm to Construct Adversarial Deformations},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2018-15},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2018/2018-15.pdf },
  year = {2018}
}

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