28 citations · 60 across the 7 of their papers we have counts for
7 papers
Boosting Adversarial Transferability by Block Shuffle and Rotation
Kunyu Wang, Xuanran He, Wenxuan Wang +1
Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferabili…
DST: Dynamic Substitute Training for Data-free Black-box Attack
Wenxuan Wang, Xuelin Qian, Yanwei Fu +1
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For data-free…
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…
Delving into Data: Effectively Substitute Training for Black-box Attack
Wenxuan Wang, Bangjie Yin, Taiping Yao +6
Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, t…
A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools
Yanwei Fu, Feng Li, Wenxuan Wang +4
The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening s…
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
Wenxuan Wang, Qiang Sun, Tao Chen +5
The recent research of facial expression recognition has made a lot of progress due to the development of deep learning technologies, but some typical challenging problems such as…
Pose-Normalized Image Generation for Person Re-identification
Xuelin Qian, Yanwei Fu, Tao Xiang +5
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features…