4 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification
Suorong Yang, Jinqiao Li, Jian Zhao +1
Data augmentation is a widely used technique for enhancing the generalization ability of convolutional neural networks (CNNs) in image classification tasks. Occlusion is a critical…
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…
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack
Jinqiao Li, Xiaotao Liu, Jian Zhao +1
Deep neural networks (DNNs) have been proven to be vulnerable to adversarial examples. A special branch of adversarial examples, namely sparse adversarial examples, can fool the ta…