Chong Xiang (Princeton University), Chawin Sitawarin (University of California, Berkeley), Tong Wu (Princeton University), Prateek Mittal (Princeton University)

ETAS Best Short Paper Award Runner-Up!

The physical-world adversarial patch attack poses a security threat to AI perception models in autonomous vehicles. To mitigate this threat, researchers have designed defenses with certifiable robustness. In this paper, we survey existing certifiably robust defenses and highlight core robustness techniques that are applicable to a variety of perception tasks, including classification, detection, and segmentation. We emphasize the unsolved problems in this space to guide future research, and call for attention and efforts from both academia and industry to robustify perception models in autonomous vehicles.

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ChargePrint: A Framework for Internet-Scale Discovery and Security Analysis...

Tony Nasr (Concordia University), Sadegh Torabi (George Mason University), Elias Bou-Harb (University of Texas at San Antonio), Claude Fachkha (University of Dubai), Chadi Assi (Concordia University)

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I Still Know What You Watched Last Sunday: Privacy...

Carlotta Tagliaro (TU Wien), Florian Hahn (University of Twente), Riccardo Sepe (Guess Europe Sagl), Alessio Aceti (Sababa Security SpA), Martina Lindorfer (TU Wien)

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