Combating Adversarial Attacks Using Sparse Representations

Code

• github.com/soorya19/sparsity-based-defenses
  OfficialIn papertf★ 0
• github.com/ZhinusMarzi/Adversarial-attack
  tf★ 0
• github.com/ZhinusMarzi/Sparsity-based-defenses-against-adversarial-attacks
  tf★ 0

Abstract

It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs). In this paper, we make the case that sparse representations of the input data are a crucial tool for combating such attacks. For linear classifiers, we show that a sparsifying front end is provably effective against _-bounded attacks, reducing output distortion due to the attack by a factor of roughly K / N where N is the data dimension and K is the sparsity level. We then extend this concept to DNNs, showing that a "locally linear" model can be used to develop a theoretical foundation for crafting attacks and defenses. Experimental results for the MNIST dataset show the efficacy of the proposed sparsifying front end.

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Reproductions