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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PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home...

Haotian Chi (Temple University), Qiang Zeng (University of South Carolina), Xiaojiang Du (Temple University), Lannan Luo (University of South Carolina)

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Differential Training: A Generic Framework to Reduce Label Noises...

Jiayun Xu (Singapore Management University), Yingjiu Li (University of Oregon), Robert H. Deng (Singapore Management University)

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Tales of Favicons and Caches: Persistent Tracking in Modern...

Konstantinos Solomos (University of Illinois at Chicago), John Kristoff (University of Illinois at Chicago), Chris Kanich (University of Illinois at Chicago), Jason Polakis (University of Illinois at Chicago)

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Detecting DolphinAttacks Based on Microphone Array

Guoming Zhang, Xiaoyu Ji (Zhejiang University)

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