Generating 3D Adversarial Point Clouds

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challenging to gen. adv. data on point clouds

  • no value to tune
  • search space being quite large
  • L_p norm not apply to point cloud data

targeted adv. attack

  • min D(x, x’) s.t. F(x’) = t’ ==> difficult!
  • min f(x’) + wD(x, x’)
    • where f(x’) = (max(Z(x’)[i]) - Z(x’)[t’])
    • diff of logits of other labels from target label
  • adv. point perturbation and adv. point gen.(new)
    • initialize to some existing points
    • and shift it while optimizing
  • adv. cluster gen.
    • small radius and close to surface

measure of point addition

  • Hausdorff distance
  • Chamfer
  • of points added

…and defence

  • PointNet more robust than tradition CNN on MNIST