Dennis Jacob, Chong Xiang, Prateek Mittal (Princeton University)

The advent of deep learning has brought about vast improvements to computer vision systems and facilitated the development of self-driving vehicles. Nevertheless, these models have been found to be susceptible to adversarial attacks. Of particular importance to the research community are patch attacks, which have been found to be realizable in the physical world. While certifiable defenses against patch attacks have been developed for tasks such as single-label classification, there does not exist a defense for multi-label classification. In this work, we propose such a defense called Multi-Label PatchCleanser, an extension of the current state-of-the-art (SOTA) method for single-label classification. We find that our approach can achieve non-trivial robustness on the MSCOCO 2014 validation dataset while maintaining high clean performance. Additionally, we leverage a key constraint between patch and object locations to develop a novel procedure and improve upon baseline robust performance.

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AutoWatch: Learning Driver Behavior with Graphs for Auto Theft...

Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

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DorPatch: Distributed and Occlusion-Robust Adversarial Patch to Evade Certifiable...

Chaoxiang He (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research), Yimiao Zeng (Huazhong University of Science and Technology), Hanqing Hu (Huazhong University of Science and Technology), Xiaofan Bai (Huazhong University of Science and Technology), Hai Jin (Huazhong University of Science and Technology), Dongmei Zhang…

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FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Meenatchi Sundaram Muthu Selva Annamalai (University College London), Igor Bilogrevic (Google), Emiliano De Cristofaro (University of California, Riverside)

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TEE-SHirT: Scalable Leakage-Free Cache Hierarchies for TEEs

Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

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