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.

View More Papers

Debunking Exposure Notification

Serge Vaudenay, EPFL, Switzerland

Read More

HERA: Hotpatching of Embedded Real-time Applications

Christian Niesler (University of Duisburg-Essen), Sebastian Surminski (University of Duisburg-Essen), Lucas Davi (University of Duisburg-Essen)

Read More

Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

Read More

Drivers and Passengers Maybe the Weakest Link in the...

Aiping Xiong (Pennsylvania State University), Zekun Cai (Pennsylvania State University) and Tianhao Wang (University of Virginia)

Read More