Divide and Conquer: Frequency-Aware Contrastive Adversarial Training for Robust Point Cloud Classification

Contrastive adversarial training has shown great potential in enhancing model robustness and has been adopted in point cloud classification. There are varying spatial distributions and densities across different regions in point cloud data, which makes adversarial perturbations always exhibit non-uniform patterns of attack intensity and distribution in different regions. However, existing approaches always rely on uniform feature contrast without considering the granularity in the context of point cloud data, limiting their capacities to counter adversarial perturbations effectively. To address this issue, we propose a novel frequency-aware contrastive adversarial training framework, which considers feature contrast via a “divide-and-conquer” method. Specifically, we systematically “<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">divide</i>” point clouds into distinct frequency components and “<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conquer</i>” feature contrast within each frequency band, which fosters fine-grained feature consistency learning and leads to more informative as well as robust representations. Besides, existing methods typically apply group-level contrastive learning, which emphasizes category-wise similarity but often overlooks the nuanced structural variations among instances. To remedy this, we incorporate instance-level contrastive learning to capture per-instance geometric variations. Moreover, a frequency-specific hard-masked sample generation module is designed to construct challenging sample pairs by masking keypoint features in each frequency band, thereby promoting the model to learn more robust feature representations. Extensive experiments on multiple benchmark datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art approaches in adversarial robustness for point cloud classification. The code is available on DiCon-FAT.