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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The Power of Bamboo: On the Post-Compromise Security for...

Tianyang Chen (Huazhong University of Science and Technology), Peng Xu (Huazhong University of Science and Technology), Stjepan Picek (Radboud University), Bo Luo (The University of Kansas), Willy Susilo (University of Wollongong), Hai Jin (Huazhong University of Science and Technology), Kaitai Liang (TU Delft)

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Accountable Javascript Code Delivery

Ilkan Esiyok (CISPA Helmholtz Center for Information Security), Pascal Berrang (University of Birmingham & Nimiq), Katriel Cohn-Gordon (Meta), Robert Künnemann (CISPA Helmholtz Center for Information Security)

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Private Certifier Intersection

Bishakh Chandra Ghosh (Indian Institute of Technology Kharagpur), Sikhar Patranabis (IBM Research - India), Dhinakaran Vinayagamurthy (IBM Research - India), Venkatraman Ramakrishna (IBM Research - India), Krishnasuri Narayanam (IBM Research - India), Sandip Chakraborty (Indian Institute of Technology Kharagpur)

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