  3D deep learning has been increasingly more popular for a variety of tasks
including many safety-critical applications. However, recently several works
raise the security issues of 3D deep models. Although most of them consider
adversarial attacks, we identify that backdoor attack is indeed a more serious
threat to 3D deep learning systems but remains unexplored. We present the
backdoor attacks in 3D point cloud with a unified framework that exploits the
unique properties of 3D data and networks. In particular, we design two attack
approaches on point cloud: the poison-label backdoor attack (PointPBA) and the
clean-label backdoor attack (PointCBA). The first one is straightforward and
effective in practice, while the latter is more sophisticated assuming there
are certain data inspections. The attack algorithms are mainly motivated and
developed by 1) the recent discovery of 3D adversarial samples suggesting the
vulnerability of deep models under spatial transformation; 2) the proposed
feature disentanglement technique that manipulates the feature of the data
through optimization methods and its potential to embed a new task. Extensive
experiments show the efficacy of the PointPBA with over 95% success rate across
various 3D datasets and models, and the more stealthy PointCBA with around 50%
success rate. Our proposed backdoor attack in 3D point cloud is expected to
perform as a baseline for improving the robustness of 3D deep models.
