15 citations · 27 across the 8 of their papers we have counts for
4 papers · 1 filter
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, Cheng Han, James Liang +3
Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant…
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
Zhiyuan Cheng, Hongjun Choi, James Liang +5
Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception, particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is t…
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, James Liang, Guanhong Tao +2
Monocular Depth Estimation (MDE) is a critical component in applications such as autonomous driving. There are various attacks against MDE networks. These attacks, especially the p…
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
Zhiyuan Cheng, James Liang, Hongjun Choi +4
Deep learning has substantially boosted the performance of Monocular Depth Estimation (MDE), a critical component in fully vision-based autonomous driving (AD) systems (e.g., Tesla…