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.

View More Papers

Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks...

Zitao Chen (University of British Columbia), Karthik Pattabiraman (University of British Columbia)

Read More

Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum...

Nicola Ruaro (University of California, Santa Barbara), Fabio Gritti (University of California, Santa Barbara), Robert McLaughlin (University of California, Santa Barbara), Ilya Grishchenko (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara)

Read More

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)

Read More

Crafter: Facial Feature Crafting against Inversion-based Identity Theft on...

Shiming Wang (Shanghai Jiao Tong University), Zhe Ji (Shanghai Jiao Tong University), Liyao Xiang (Shanghai Jiao Tong University), Hao Zhang (Shanghai Jiao Tong University), Xinbing Wang (Shanghai Jiao Tong University), Chenghu Zhou (Chinese Academy of Sciences), Bo Li (Hong Kong University of Science and Technology)

Read More