Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis. (arXiv:2205.08738v1 [cs.MM])

Nowadays, 3D data plays an indelible role in the computer vision field.
However, extensive studies have proved that deep neural networks (DNNs) fed
with 3D data, such as point clouds, are susceptible to adversarial examples,
which aim to misguide DNNs and might bring immeasurable losses. Currently, 3D
adversarial point clouds are chiefly generated in three fashions, i.e., point
shifting, point adding, and point dropping. These point manipulations would
modify geometrical properties and local correlations of benign point clouds
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3d arxiv defense