5 citations · 9 across the 8 of their papers we have counts for
8 papers
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
Region-Guided Attack on the Segment Anything Model (SAM)
Xiaoliang Liu, Furao Shen, Jian Zhao
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and m…
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition
Xiaoliang Liu, Furao Shen, Jian Zhao +1
Face recognition (FR) systems powered by deep learning have become widely used in various applications. However, they are vulnerable to adversarial attacks, especially those based…
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Xiaoliang Liu, Furao Shen, Feng Han +2
Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns. Existing research p…
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
Xiaoliang Liu, Furao Shen, Jian Zhao +1
Deep neural networks are powerful, but they also have shortcomings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliabilit…
RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer
Xiaoliang Liu, Furao Shen, Jian Zhao +1
Face recognition has achieved considerable progress in recent years thanks to the development of deep neural networks, but it has recently been discovered that deep neural networks…
RandoMix: A mixed sample data augmentation method with multiple mixed modes
Xiaoliang Liu, Furao Shen, Jian Zhao +1
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-samp…
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
Andre Goncalves, Xiaoli Liu, Arindam Banerjee
Alternating Direction Method of Multipliers (ADMM) has become a widely used optimization method for convex problems, particularly in the context of data mining in which large optim…