Yulong Cao (University of Michigan), Ningfei Wang (UC, Irvine), Chaowei Xiao (Arizona State University), Dawei Yang (University of Michigan), Jin Fang (Baidu Research), Ruigang Yang (University of Michigan), Qi Alfred Chen (UC, Irvine), Mingyan Liu (University of Michigan) and Bo Li (University of Illinois at Urbana-Champaign)

In autonomous driving (AD) vehicles, Multi-Sensor Fusion (MSF) is used to combine perception results from multiple sensors such as LiDARs (Light Detection And Ranging) and cameras for both accuracy and robustness. In this work, we design the first attack that fundamentally defeats MSF-based AD perception by generating 3D adversarial objects. This demonstration will include video and figure demonstrations for the generated 3D adversarial objects and the end-to-end consequences.

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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Chhoyhopper: A Moving Target Defense with IPv6

A S M Rizvi (University of Southern California/Information Sciences Institute) and John Heidemann (University of Southern California/Information Sciences Institute)

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Demo #4: Recovering Autonomous Robotic Vehicles from Physical Attacks

Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

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HARPO: Learning to Subvert Online Behavioral Advertising

Jiang Zhang (University of Southern California), Konstantinos Psounis (University of Southern California), Muhammad Haroon (University of California, Davis), Zubair Shafiq (University of California, Davis)

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