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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GTrans: Graph Transformer-Based Obfuscation-resilient Binary Code Similarity Detection

Yun Zhang (Hunan University), Yuling Liu (Hunan University), Ge Cheng (Xiangtan University), Bo Ou (Hunan University)

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Towards generic backward-compatible software upgrades for COSPAS-SARSAT EPIRB 406...

Ahsan Saleem (University of Jyväskylä, Finland), Andrei Costin (University of Jyväskylä, Finland), Hannu Turtiainen (University of Jyväskylä, Finland), Timo Hämäläinen (University of Jyväskylä, Finland)

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WIP: Shadow Hack: Adversarial Shadow Attack Against LiDAR Object...

Ryunosuke Kobayashi, Kazuki Nomoto, Yuna Tanaka, Go Tsuruoka (Waseda University), Tatsuya Mori (Waseda University/NICT/RIKEN)

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CamPro: Camera-based Anti-Facial Recognition

Wenjun Zhu (Zhejiang University), Yuan Sun (Zhejiang University), Jiani Liu (Zhejiang University), Yushi Cheng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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