Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

As a fundamental task in autonomous driving, LiDAR semantic segmentation aims to provide semantic understanding of the driving environment. We demonstrate that existing LiDAR semantic segmentation models in autonomous driving systems can be easily fooled by placing some simple objects on the road, such as cardboard and traffic signs. We show that this type of attack can hide a vehicle and change the road surface to road-side vegetation.

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Demo #8: Security of Camera-based Perception for Autonomous Driving...

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

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Cross-Language Attacks

Samuel Mergendahl (MIT Lincoln Laboratory), Nathan Burow (MIT Lincoln Laboratory), Hamed Okhravi (MIT Lincoln Laboratory)

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RamBoAttack: A Robust and Query Efficient Deep Neural Network...

Viet Quoc Vo (The University of Adelaide), Ehsan Abbasnejad (The University of Adelaide), Damith C. Ranasinghe (University of Adelaide)

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VPNInspector: Systematic Investigation of the VPN Ecosystem

Reethika Ramesh (University of Michigan), Leonid Evdokimov (Independent), Diwen Xue (University of Michigan), Roya Ensafi (University of Michigan)

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