3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search
Nima Jamali, Matina Mahdizadeh Sani, Hanieh Naderi +1
Deep neural networks (DNNs) have demonstrated remarkable performance in analyzing 3D point cloud data. However, their vulnerability to adversarial attacks-such as point dropping, s…
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey
Hanieh Naderi, Ivan V. Bajić
Deep learning has successfully solved a wide range of tasks in 2D vision as a dominant AI technique. Recently, deep learning on 3D point clouds is becoming increasingly popular for…
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks
Hanieh Naderi, Chinthaka Dinesh, Ivan V. Bajic +1
Adversarial attacks pose serious challenges for deep neural network (DNN)-based analysis of various input signals. In the case of three-dimensional point clouds, methods have been…
LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
Hanieh Naderi, Kimia Noorbakhsh, Arian Etemadi +1
Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks. This increases the…
Adversarial Attack by Limited Point Cloud Surface Modifications
Atrin Arya, Hanieh Naderi, Shohreh Kasaei
Recent research has revealed that the security of deep neural networks that directly process 3D point clouds to classify objects can be threatened by adversarial samples. Although…
Generating Unrestricted Adversarial Examples via Three Parameters
Hanieh Naderi, Leili Goli, Shohreh Kasaei
Deep neural networks have been shown to be vulnerable to adversarial examples deliberately constructed to misclassify victim models. As most adversarial examples have restricted th…