Attacking Point Cloud Segmentation with Color-only Perturbation
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Recent research efforts on 3D point-cloud semantic segmentation have achieved outstanding performance by adopting deep CNN (convolutional neural networks) and GCN (graph convolutional networks). However, the robustness of these complex models has not been systematically analyzed. Given that semantic segmentation has been applied in many safety-critical applications (e.g., autonomous driving, geological sensing), it is important to fill this knowledge gap, in particular, how these models are affected under adversarial samples. While adversarial attacks against point cloud have been studied, we found all of them were targeting single-object recognition, and the perturbation is done on the point coordinates. We argue that the coordinate-based perturbation is unlikely to realize under the physical-world constraints. Hence, we propose a new color-only perturbation method named COLPER, and tailor it to semantic segmentation. By evaluating COLPER on an indoor dataset (S3DIS) and an outdoor dataset (Semantic3D) against three point cloud segmentation models (PointNet++, DeepGCNs, and RandLA-Net), we found color-only perturbation is sufficient to significantly drop the segmentation accuracy and aIoU, under both targeted and non-targeted attack settings.
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