Congratualtions!

Our paper has been accepted to the IEEE Trans. Information Forensics and Security (TIFS) (JCR Top 7.8%, Impact Factor: 8.0) [LINK]

  • Title: Deformable 3D Point Cloud Perturbations using Cage-based Deformation for Semantic Consistency

  • Authors: Kyo Seok Lee and Hak Gu Kim

  • Abstract: Deep neural networks for 3D point cloud analysis are widely used in applications such as autonomous driving and robotics, yet they remain highly vulnerable to adversarial attacks. Existing methods typically minimize point-wise distances to preserve geometry, which constrains perturbations and leads to a trade-off between imperceptibility and attack strength. To address this limitation, we propose a cage-based adversarial deformation framework that generates semantically consistent perturbations aligned with natural intra-class variations. Our method refines a source cage, predicts adversarial cage displace ments by fusing source–target features, and computes smooth point-wise offsets using solid-angle– and distance-aware weights. This enables globally coherent deformations that appear natural to humans while effectively misleading classifiers. Experiments on ModelNet40 and ShapeNet-Part show that our approach achieves state-of-the-art attack success rates while producing the most uniform point distributions and lowest local distortions. Furthermore, the perturbations remain effective against common defenses such as SRS, SOR, and DUP-Net.