PANDA: Diffusion-Guided Purification and Adaptation for Robust Point Cloud Classification Against Adversarial Attack

Summary

Deep learning point cloud classifiers are highly vulnerable to adversarial attacks, and existing diffusion-based purification defenses have a distributional gap and a semantic mismatch. The authors propose PANDA, a two-stage framework: PANDA-P trains a dual-branch diffusion purifier on both clean-to-clean and adversarial-to-clean paths, and PANDA-A fine-tunes the classifier with a consistency-driven objective to recalibrate its decision boundary. The source reports consistently superior robustness over existing purification-based defenses on synthetic and real-world benchmarks.

Topics

• Adversarial machine learning

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