Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness

Backdoor attacks pose a severe threat to deep neural networks (DNN) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously. Existing 3D point cloud backdoor attacks primarily rely on sample-wise global modifications, resulting in suboptimal stealthiness. To address this limitation, we propose Stealthy Patch-Wise Backdoor Attack (SPBA), which employs the first patch-wise trigger for 3D point clouds and restricts perturbations to local regions, significantly enhancing stealthiness. Specifically, SPBA decomposes point clouds into local patches and evaluates their geometric complexity using a curvature-based patch imperceptibility score, ensuring that the trigger remains less perceptible to the human eye by strategically applying it across multiple geometrically complex patches with lower visual sensitivity. By leveraging the Graph Fourier Transform (GFT), SPBA optimizes a patch-wise spectral trigger that perturbs the spectral features of selected patches, enhancing attack effectiveness while preserving the global geometric structure of the point cloud. Extensive experiments on ModelNet40 and ShapeNetPart demonstrate that SPBA consistently achieves an attack success rate (ASR) exceeding 96.5% across different models while achieving state-of-the-art imperceptibility compared to existing backdoor attack methods.