RobNAS: Robust Neural Architecture Search for Point Cloud Adversarial Defense

As point clouds gain widespread application in fields such as autonomous driving and scene modeling, an increasing number of point cloud learning networks have emerged. As a result, research on 3D adversarial attacks and defenses has rapidly advanced. To the best of our knowledge, existing 3D defense methods primarily focus on enhancing the robustness of networks through point cloud processing or adversarial training, without attention given to network architecture. In this paper, we propose RobNAS, enhancing the adversarial robustness of point cloud classification networks by integrating adversarial training with Neural Architecture Search (NAS) from an architectural robustness perspective. Specifically, we first incorporate PGD-based adversarial training during the architecture search phase of RobNAS to obtain the most robust architecture. Subsequently, during the adversarial training phase, we introduce various adversarial examples to enhance the robustness of the model weights. Our experimental results demonstrate that our method achieves State-Of-The-Art (SOTA) performance. Furthermore, we aim to shed light on the promising potential of architectural robustness for learning robust point cloud representation.