  The increasing adoption of 3D point cloud data in various applications, such
as autonomous vehicles, robotics, and virtual reality, has brought about
significant advancements in object recognition and scene understanding.
However, this progress is accompanied by new security challenges, particularly
in the form of backdoor attacks. These attacks involve inserting malicious
information into the training data of machine learning models, potentially
compromising the model's behavior. In this paper, we propose CloudFort, a novel
defense mechanism designed to enhance the robustness of 3D point cloud
classifiers against backdoor attacks. CloudFort leverages spatial partitioning
and ensemble prediction techniques to effectively mitigate the impact of
backdoor triggers while preserving the model's performance on clean data. We
evaluate the effectiveness of CloudFort through extensive experiments,
demonstrating its strong resilience against the Point Cloud Backdoor Attack
(PCBA). Our results show that CloudFort significantly enhances the security of
3D point cloud classification models without compromising their accuracy on
benign samples. Furthermore, we explore the limitations of CloudFort and
discuss potential avenues for future research in the field of 3D point cloud
security. The proposed defense mechanism represents a significant step towards
ensuring the trustworthiness and reliability of point-cloud-based systems in
real-world applications.
