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Geometry-Aware Generation of Adversarial Point Clouds
[article]
2020
arXiv
pre-print
Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain, a few recent attempts have been made to extend them to 3D point cloud data. However, adversarial results obtained by these methods typically contain point outliers, which are both noticeable and easy to defend against using the simple techniques of outlier removal. Motivated by the different mechanisms by which
arXiv:1912.11171v3
fatcat:moalrm26nnd2vnedlcwqvkvema