Adversarial attacks pose serious challenges for deep neural network
(DNN)-based analysis of various input signals. In the case of 3D point clouds,
methods have been developed to identify points that play a key role in the
network decision, and these become crucial in generating existing adversarial
attacks. For example, a saliency map approach is a popular method for
identifying adversarial drop points, whose removal would significantly impact
the network decision. Generally, methods for identifying adversarial points
rely on the deep model itself in order to determine which points are critically
important for the model's decision. This paper aims to provide a novel
viewpoint on this problem, in which adversarial points can be predicted
independently of the model. To this end, we define 14 point cloud features and
use multiple linear regression to examine whether these features can be used
for model-free adversarial point prediction, and which combination of features
is best suited for this purpose. Experiments show that a suitable combination
of features is able to predict adversarial points of three different networks
-- PointNet, PointNet++, and DGCNN -- significantly better than a random guess.
The results also provide further insight into DNNs for point cloud analysis, by
showing which features play key roles in their decision-making process.

本研究提供了一种新的观点解决深度神经网络在三维点云中对敌对攻击的防御问题。研究采用多元线性回归分析 14 个点云特征，寻找最佳组合以实现模型无关的对抗点预测。实验结果证明该方法可以在 PointNet、PointNet++ 和 DGCNN 三种神经网络中更好地预测对抗点，并揭示哪些特征在它们的决策过程中起着关键作用。