Kanglan Tang, Junjie Shen, and Qi Alfred Chen (UC Irvine)

The perception module is the key to the security of Autonomous Driving systems. It perceives the environment through sensors to help make safe and correct driving decisions on the road. The localization module is usually considered to be independent of the perception module. However, we discover that the correctness of perception output highly depends on localization due to the widely used Region-of-Interest design adopted in perception. Leveraging this insight, we propose an ROI attack and perform a case study in the traffic light detection in Autonomous Driving systems. We evaluate the ROI attack on a production-grade Autonomous Driving system, named Baidu Apollo, under end-to-end simulation environments. We found our attack is able to make the victim a red light runner or cause denial-of-service with a 100% success rate.

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Debunking Exposure Notification

Serge Vaudenay, EPFL, Switzerland

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Mondrian: Comprehensive Inter-domain Network Zoning Architecture

Jonghoon Kwon (ETH Zürich), Claude Hähni (ETH Zürich), Patrick Bamert (Zürcher Kantonalbank), Adrian Perrig (ETH Zürich)

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Rosita: Towards Automatic Elimination of Power-Analysis Leakage in Ciphers

Madura A. Shelton (University of Adelaide), Niels Samwel (Radboud University), Lejla Batina (Radboud University), Francesco Regazzoni (University of Amsterdam and ALaRI – USI), Markus Wagner (University of Adelaide), Yuval Yarom (University of Adelaide and Data61)

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Towards Understanding and Detecting Cyberbullying in Real-world Images

Nishant Vishwamitra (University at Buffalo), Hongxin Hu (University at Buffalo), Feng Luo (Clemson University), Long Cheng (Clemson University)

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