  Despite the great progress of 3D vision, data privacy and security issues in
3D deep learning are not explored systematically. In the domain of 2D images,
many availability attacks have been proposed to prevent data from being
illicitly learned by unauthorized deep models. However, unlike images
represented on a fixed dimensional grid, point clouds are characterized as
unordered and unstructured sets, posing a significant challenge in designing an
effective availability attack for 3D deep learning. In this paper, we
theoretically show that extending 2D availability attacks directly to 3D point
clouds under distance regularization is susceptible to the degeneracy,
rendering the generated poisons weaker or even ineffective. This is because in
bi-level optimization, introducing regularization term can result in update
directions out of control. To address this issue, we propose a novel Feature
Collision Error-Minimization (FC-EM) method, which creates additional shortcuts
in the feature space, inducing different update directions to prevent the
degeneracy of bi-level optimization. Moreover, we provide a theoretical
analysis that demonstrates the effectiveness of the FC-EM attack. Extensive
experiments on typical point cloud datasets, 3D intracranial aneurysm medical
dataset, and 3D face dataset verify the superiority and practicality of our
approach. Code is available at https://github.com/hala64/fc-em.
