Abstract
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.
Abstract (translated)
3D点云数据的日益广泛应用，如自动驾驶、机器人学和虚拟现实，带来了物体识别和场景理解方面的显著进步。然而，这一进步伴随着新的安全挑战，特别是后门攻击。这些攻击涉及在机器学习模型的训练数据中插入恶意信息，可能危及模型的行为。在本文中，我们提出了CloudFort，一种专门设计用于增强3D点云分类器对后门攻击的鲁棒性的新颖防御机制。CloudFort利用空间分割和集成预测技术，有效减轻后门触发器对模型的影响，同时保留模型在干净数据上的性能。我们通过广泛的实验评估了CloudFort的有效性，证明了它对点云后门攻击（PCBA）具有很强的抵抗力。我们的结果表明，CloudFort显著增强了不牺牲准确性的3D点云分类模型的安全性。此外，我们探讨了CloudFort的局限性，并讨论了该领域未来研究的潜在方向。所提出的防御机制在确保基于点云的系统的可靠性和可信度方面迈出了重要的一步。
URL
https://arxiv.org/abs/2404.14042
