Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

Advanced driver-assistance systems (ADAS) are widely used by modern vehicle manufacturers to automate, adapt and enhance vehicle technology for safety and better driving. In this work, we design a practical attack against automated lane centering (ALC), a crucial functionality of ADAS, with remote adversarial patches. We identify that the back of a vehicle is an effective attack vector and improve the attack robustness by considering various input frames. The demo includes videos that show our attack can divert victim vehicle out of lane on a representative ADAS, Openpilot, in a simulator.

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An In-Depth Analysis on Adoption of Attack Mitigations in...

Ruotong Yu (Stevens Institute of Technology, University of Utah), Yuchen Zhang, Shan Huang (Stevens Institute of Technology)

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Hybrid Trust Multi-party Computation with Trusted Execution Environment

Pengfei Wu (School of Computing, National University of Singapore), Jianting Ning (College of Computer and Cyber Security, Fujian Normal University; Institute of Information Engineering, Chinese Academy of Sciences), Jiamin Shen (School of Computing, National University of Singapore), Hongbing Wang (School of Computing, National University of Singapore), Ee-Chien Chang (School of Computing, National University of Singapore)

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DITTANY: Strength-Based Dynamic Information Flow Analysis Tool for x86...

Walid J. Ghandour, Clémentine Maurice (CNRS, CRIStAL)

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Effects of Knowledge and Experience on Privacy Decision-Making in...

Zekun Cai (Penn State University), Aiping Xiong (Penn State University)

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