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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Why People Still Fall for Phishing Emails: An Empirical...

Asangi Jayatilaka (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, School of Computing Technologies, RMIT University), Nalin Asanka Gamagedara Arachchilage (School of Computer Science, The University of Auckland), M. Ali Babar (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide)

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On Precisely Detecting Censorship Circumvention in Real-World Networks

Ryan Wails (Georgetown University, U.S. Naval Research Laboratory), George Arnold Sullivan (University of California, San Diego), Micah Sherr (Georgetown University), Rob Jansen (U.S. Naval Research Laboratory)

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ActiveDaemon: Unconscious DNN Dormancy and Waking Up via User-specific...

Ge Ren (Shanghai Jiao Tong University), Gaolei Li (Shanghai Jiao Tong University), Shenghong Li (Shanghai Jiao Tong University), Libo Chen (Shanghai Jiao Tong University), Kui Ren (Zhejiang University)

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Efficient and Timely Revocation of V2X Credentials

Gianluca Scopelliti (Ericsson & KU Leuven), Christoph Baumann (Ericsson), Fritz Alder (KU Leuven), Eddy Truyen (KU Leuven), Jan Tobias Mühlberg (Université libre de Bruxelles & KU Leuven)

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