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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Reynaldo Morillo (University of Connecticut), Justin Furuness (University of Connecticut), Cameron Morris (University of Connecticut), James Breslin (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut)

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Paul Agbaje (University of Texas at Arlington), Afia Anjum (University of Texas at Arlington), Arkajyoti Mitra (University of Texas at Arlington), Gedare Bloom (University of Colorado Colorado Springs) and Habeeb Olufowobi (University of Texas at Arlington)

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Mohit Kumar Jangid (Ohio State University) and Zhiqiang Lin (Ohio State University)

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Michael Lutaaya, Hala Assal, Khadija Baig, Sana Maqsood, Sonia Chiasson (Carleton University)

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