On The Relationship Between Universal Adversarial Attacks And Sparse
  Representations
Dana Weitzner Raja Giryes
The prominent success of neural networks, mainly in computer vision tasks, is
increasingly shadowed by their sensitivity to small, barely perceivable
adversarial perturbations in image input.
  In this work, we aim at explaining this vulnerability through the framework
of sparsity.
  We show the connection between adversarial attacks and sparse
representations, with a focus on explaining the universality and
transferability of adversarial examples in neural networks.
  To this end, we show that sparse coding algorithms, and the neural
network-based learned iterative shrinkage thresholding algorithm (LISTA) among
them, suffer from this sensitivity, and that common attacks on neural networks
can be expressed as attacks on the sparse representation of the input image.
The phenomenon that we observe holds true also when the network is agnostic to
the sparse representation and dictionary, and thus can provide a possible
explanation for the universality and transferability of adversarial attacks.
  The code is available at
https://github.com/danawr/adversarial_attacks_and_sparse_representations.