7 citations · 20 across the 9 of their papers we have counts for
9 papers
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
Xiao Li, Wenxuan Sun, Huanran Chen +5
Recently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs t…
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…
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…
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Xiao Li, Wei Zhang, Yining Liu +3
Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting…
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…
On the Privacy Effect of Data Enhancement via the Lens of Memorization
Xiao Li, Qiongxiu Li, Zhanhao Hu +1
Machine learning poses severe privacy concerns as it has been shown that the learned models can reveal sensitive information about their training data. Many works have investigated…
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Chufeng Tang, Hang Chen, Xiao Li +3
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to t…
Rethinking Natural Adversarial Examples for Classification Models
Xiao Li, Jianmin Li, Ting Dai +3
Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples"…
Fooling thermal infrared pedestrian detectors in real world using small bulbs
Xiaopei Zhu, Xiao Li, Jianmin Li +2
Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. They have the unique advantages…