1 citations · 1 across the 6 of their papers we have counts for
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A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
Wei Zhang, Zhanhao Hu, Xiao Li +2
In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers…
PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection
Xiao Li, Yiming Zhu, Yifan Huang +4
Object detection plays a crucial role in many security-sensitive applications. However, several recent studies have shown that object detectors can be easily fooled by physically r…
PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition
Xiao Li, Yining Liu, Na Dong +2
Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based…
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
Zhanhao Hu, Wenda Chu, Xiaopei Zhu +3
Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans.…
On the Importance of Backbone to the Adversarial Robustness of Object Detectors
Xiao Li, Hang Chen, Xiaolin Hu
Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing object detectors are vulne…
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks
Xiao Li, Ziqi Wang, Bo Zhang +2
Adversarial attacks can easily fool object recognition systems based on deep neural networks (DNNs). Although many defense methods have been proposed in recent years, most of them…