  Deep neural networks have made tremendous progress in 3D point-cloud
recognition. Recent works have shown that these 3D recognition networks are
also vulnerable to adversarial samples produced from various attack methods,
including optimization-based 3D Carlini-Wagner attack, gradient-based iterative
fast gradient method, and skeleton-detach based point-dropping. However, after
a careful analysis, these methods are either extremely slow because of the
optimization/iterative scheme, or not flexible to support targeted attack of a
specific category. To overcome these shortcomings, this paper proposes a novel
label guided adversarial network (LG-GAN) for real-time flexible targeted point
cloud attack. To the best of our knowledge, this is the first generation based
3D point cloud attack method. By feeding the original point clouds and target
attack label into LG-GAN, it can learn how to deform the point clouds to
mislead the recognition network into the specific label only with a single
forward pass. In detail, LGGAN first leverages one multi-branch adversarial
network to extract hierarchical features of the input point clouds, then
incorporates the specified label information into multiple intermediate
features using the label encoder. Finally, the encoded features will be fed
into the coordinate reconstruction decoder to generate the target adversarial
sample. By evaluating different point-cloud recognition models (e.g., PointNet,
PointNet++ and DGCNN), we demonstrate that the proposed LG-GAN can support
flexible targeted attack on the fly while guaranteeing good attack performance
and higher efficiency simultaneously.
