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Many efforts have been made on developing adversarial attack methods on point clouds. However, without fully considering the geometric property of point clouds, existing methods tend to produce clearly visible outliers. In this paper, we propose a novel NormalAttack framework towards imperceptible adversarial attacks on point clouds. First, we enforce the perturbation to be concentrated along normals to deform the underlying surface of 3D point clouds, such that tiny perturbation can make the shape deformed for better attack performance. Second, we guide the perturbation to be located more on regions with larger curvature, such that better imperceptibility is achieved. Extensive experiments on three representative networks, e.g., PointNet++, DGCNN, and PointConv, validate the effectiveness of NormalAttack and its superiority to state-of-the-art methods.

Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.

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