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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Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

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CANCloak: Deceiving Two ECUs with One Frame

Li Yue, Zheming Li, Tingting Yin, and Chao Zhang (Tsinghua University)

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(Short) WIP: Deployability Improvement, Stealthiness User Study, and Safety...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

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Detecting Kernel Memory Leaks in Specialized Modules with Ownership...

Navid Emamdoost (University of Minnesota), Qiushi Wu (University of Minnesota), Kangjie Lu (University of Minnesota), Stephen McCamant (University of Minnesota)

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