  3D deep models consuming point clouds have achieved sound application effects
in computer vision. However, recent studies have shown they are vulnerable to
3D adversarial point clouds. In this paper, we regard these malicious point
clouds as 3D steganography examples and present a new perspective, 3D
steganalysis, to counter such examples. Specifically, we propose 3D-VFD, a
victim-free detector against 3D adversarial point clouds. Its core idea is to
capture the discrepancies between residual geometric feature distributions of
benign point clouds and adversarial point clouds and map these point clouds to
a lower dimensional space where we can efficiently distinguish them. Unlike
existing detection techniques against 3D adversarial point clouds, 3D-VFD does
not rely on the victim 3D deep model's outputs for discrimination. Extensive
experiments demonstrate that 3D-VFD achieves state-of-the-art detection and can
effectively detect 3D adversarial attacks based on point adding and point
perturbation while keeping fast detection speed.
