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

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.

View More Papers

Parakeet: Practical Key Transparency for End-to-End Encrypted Messaging

Harjasleen Malvai (UIUC/IC3), Lefteris Kokoris-Kogias (IST Austria), Alberto Sonnino (Mysten Labs), Esha Ghosh (Microsoft Research), Ercan Oztürk (Meta), Kevin Lewi...

Read More

Location Spoofing Attacks on Autonomous Fleets

Jinghan Yang, Andew Estornell, Yevgeniy Vorobeychik (Washington University in St. Louis)

Read More

Thwarting Smartphone SMS Attacks at the Radio Interface Layer

Haohuang Wen (Ohio State University), Phillip Porras (SRI International), Vinod Yegneswaran (SRI International), Zhiqiang Lin (Ohio State University)

Read More

Navigating Murky Waters: Automated Browser Feature Testing for Uncovering...

Mir Masood Ali (University of Illinois Chicago), Binoy Chitale (Stony Brook University), Mohammad Ghasemisharif (University of Illinois Chicago), Chris Kanich...

Read More