# Minimal Adversarial Examples for Deep Learning on 3D Point Clouds Source: [Minimal Adversarial Examples for Deep Learning on 3D Point Clouds]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:32:17.810Z Authors: Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung Published: 2020-08-27T11:50:45.000Z ## Abstract With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e.g., object recognition, semantic segmentation. In a safety-critical environment, it is however not well understood how such deep learning models are vulnerable to adversarial examples. In this work, we explore adversarial attacks for point cloud-based neural networks. We propose a unified formulation for adversarial point cloud generation that can generalise two different attack strategies. Our method generates adversarial examples by attacking the classification ability of point cloud-based networks while considering the perceptibility of the examples and ensuring the minimal level of point manipulations. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively, while manipulating only about 4% of the total points. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Point Cloud Classification on ScanObjectNN (test)](): 3D Point Cloud Classification; ScanObjectNN (test); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()