The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite
various prior studies on its security issues, all of them only consider attacks on camera-or …

Most prior works on physical adversarial attacks mainly focus on the attack performance but
seldom enforce any restrictions over the appearance of the generated adversarial patches …

Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …

Recent studies have demonstrated that existing deep neural networks (DNNs) on 3D point
clouds are vulnerable to adversarial examples, especially under the white-box settings …

As 3D point cloud analysis has received increasing attention, the insufficient scale of point
cloud datasets and the weak generalization ability of networks become prominent. In this …

Deep neural networks are vulnerable to adversarial attacks, in which imperceptible
perturbations to their input lead to erroneous network predictions. This phenomenon has …

Recently, several attempts have demonstrated that 3D deep learning models are as
vulnerable to adversarial example attacks as 2D models. However, these methods are still …

Multiview Robust Adversarial Stickers for Arbitrary Objects in the Physical World Page 1
Received: 13 July 2022 | Revised: 18 July 2022 | Accepted: 24 August 2022 | Published online …