  For saving cost, many deep neural networks (DNNs) are trained on third-party
datasets downloaded from internet, which enables attacker to implant backdoor
into DNNs. In 2D domain, inherent structures of different image formats are
similar. Hence, backdoor attack designed for one image format will suite for
others. However, when it comes to 3D world, there is a huge disparity among
different 3D data structures. As a result, backdoor pattern designed for one
certain 3D data structure will be disable for other data structures of the same
3D scene. Therefore, this paper designs a uniform backdoor pattern: NRBdoor
(Noisy Rotation Backdoor) which is able to adapt for heterogeneous 3D data
structures. Specifically, we start from the unit rotation and then search for
the optimal pattern by noise generation and selection process. The proposed
NRBdoor is natural and imperceptible, since rotation is a common operation
which usually contains noise due to both the miss match between a pair of
points and the sensor calibration error for real-world 3D scene. Extensive
experiments on 3D mesh and point cloud show that the proposed NRBdoor achieves
state-of-the-art performance, with negligible shape variation.
