Hard-Label Black-Box Attacks on 3D Point Clouds

Summary

This research presents a hard-label black-box attack on 3D point cloud models, where the attacker can only observe the predicted label of an input. The method builds a class-aware decision boundary through a learnable spectrum-fusion strategy, then uses iterative coordinate-spectrum optimization with curvature-aware boundary search to produce adversarial point clouds with trivial perturbations. The authors report that the attack competitively outperforms existing white-box and black-box attackers in attack performance and adversary quality.

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