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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PriSrv: Privacy-Enhanced and Highly Usable Service Discovery in Wireless...

Yang Yang (School of Computing and Information Systems, Singapore Management University, Singapore), Robert H. Deng (School of Computing and Information Systems, Singapore Management University, Singapore), Guomin Yang (School of Computing and Information Systems, Singapore Management University, Singapore), Yingjiu Li (Department of Computer Science, University of Oregon, USA), HweeHwa Pang (School of Computing and Information Systems,…

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Unus pro omnibus: Multi-Client Searchable Encryption via Access Control

Jiafan Wang (Data61, CSIRO), Sherman S. M. Chow (The Chinese University of Hong Kong)

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WIP: Adversarial Object-Evasion Attack Detection in Autonomous Driving Contexts:...

Rao Li (The Pennsylvania State University), Shih-Chieh Dai (Pennsylvania State University), Aiping Xiong (Penn State University)

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BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

Cameron Morris (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut), Samuel Secondo (University of Connecticut)

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