Christopher DiPalma, Ningfei Wang, Takami Sato, and Qi Alfred Chen (UC Irvine)

Robust perception is crucial for autonomous vehicle security. In this work, we design a practical adversarial patch attack against camera-based obstacle detection. We identify that the back of a box truck is an effective attack vector. We also improve attack robustness by considering a variety of input frames associated with the attack scenario. This demo includes videos that show our attack can cause end-to-end consequences on a representative autonomous driving system in a simulator.

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Practical Non-Interactive Searchable Encryption with Forward and Backward Privacy

Shi-Feng Sun (Monash University, Australia), Ron Steinfeld (Monash University, Australia), Shangqi Lai (Monash University, Australia), Xingliang Yuan (Monash University, Australia),...

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Demo #1: Curricular Reinforcement Learning for Robust Policy in...

Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

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GPSKey: GPS based Secret Key Establishment for Intra-Vehicle Environment

Edwin Yang (University of Oklahoma) and Song Fang (University of Oklahoma)

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Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving

Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New...

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