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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Evaluating Disassembly Ground Truth Through Dynamic Tracing (abstract)

Lambang Akbar (National University of Singapore), Yuancheng Jiang (National University of Singapore), Roland H.C. Yap (National University of Singapore), Zhenkai Liang (National University of Singapore), Zhuohao Liu (National University of Singapore)

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VPN Awareness and Misconceptions: A Comparative Study in Canadian...

Lachlan Moore, Tatsuya Mori (Waseda University, NICT)

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Threats Against Satellite Ground Infrastructure: A retrospective analysis of...

Jessie Hamill-Stewart (University of Bristol and University of Bath), Awais Rashid (University of Bristol)

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IdleLeak: Exploiting Idle State Side Effects for Information Leakage

Fabian Rauscher (Graz University of Technology), Andreas Kogler (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Daniel Gruss (Graz University of Technology)

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