39 citations · 39 across the 2 of their papers we have counts for
4 papers
Adversarial Robustness under Long-Tailed Distribution
Tong Wu, Ziwei Liu, Qingqiu Huang +2
Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversaria…
Towards Evaluating and Training Verifiably Robust Neural Networks
Zhaoyang Lyu, Minghao Guo, Tong Wu +3
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP train…
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification
Weitao Wan, Jiansheng Chen, Cheng Yu +3
The softmax cross-entropy loss function has been widely used to train deep models for various tasks. In this work, we propose a Gaussian mixture (GM) loss function for deep neural…
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
Tong Wu, Xuefei Ning, Wenshuo Li +3
In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on ve…