Hard-Label Black-Box Attacks on 3D Point Clouds
Daizong Liu, Yunbo Tao, Junhao Dong, Keke Tang, Pan Zhou, Wei Hu, Yew-Soon Ong

TL;DR
This paper introduces a practical hard-label black-box attack method on 3D point cloud models, leveraging spectral domain techniques to generate high-quality adversarial samples without model details.
Contribution
The authors propose a novel spectrum-aware decision boundary algorithm and an iterative optimization method for effective black-box attacks on 3D point clouds.
Findings
Outperforms existing white/black-box attackers in attack success and sample quality.
Uses spectral domain fusion to craft intermediate samples without geometric distortion.
Demonstrates effectiveness in real-world scenarios with limited model access.
Abstract
With the maturity of depth sensors in various 3D safety-critical applications, 3D point cloud models have been shown to be vulnerable to adversarial attacks. Almost all existing 3D attackers simply follow the white-box or black-box setting to iteratively update coordinate perturbations based on back-propagated or estimated gradients. However, these methods are hard to deploy in real-world scenarios (no model details are provided) as they severely rely on parameters or output logits of victim models. To this end, we propose point cloud attacks from a more practical setting, i.e., hard-label black-box attack, in which attackers can only access the prediction label of 3D input. We introduce a novel 3D attack method based on a new spectrum-aware decision boundary algorithm to generate high-quality adversarial samples. In particular, we first construct a class-aware model decision boundary,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · 3D Shape Modeling and Analysis
