13 citations · 20 across the 5 of their papers we have counts for
5 papers
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Bangjie Yin, Wenxuan Wang, Taiping Yao +5
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples. However, existing adversarial exampl…
LINTS^RT: A Learning-driven Testbed for Intelligent Scheduling in Embedded Systems
Zelun Kong, Yaswanth Yadlapalli, Soroush Bateni +2
Due to the increasing complexity seen in both workloads and hardware resources in state-of-the-art embedded systems, developing efficient real-time schedulers and the corresponding…
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks
Junfeng Guo, Ting Wang, Cong Liu
The black-box nature of deep neural networks (DNNs) facilitates attackers to manipulate the behavior of DNN through data poisoning. Being able to detect and mitigate poisoning atta…
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
Zelun Kong, Junfeng Guo, Ang Li +1
Although Deep neural networks (DNNs) are being pervasively used in vision-based autonomous driving systems, they are found vulnerable to adversarial attacks where small-magnitude p…
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
Zhuoyi Wang, Zelun Kong, Hemeng Tao +2
One of the key challenges of performing label prediction over a data stream concerns with the emergence of instances belonging to unobserved class labels over time. Previously, thi…