3 citations · 8 across the 5 of their papers we have counts for
5 papers
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
Ruoxi Qin, Linyuan Wang, Xuehui Du +2
The deep neural network has attained significant efficiency in image recognition. However, it has vulnerable recognition robustness under extensive data uncertainty in practical ap…
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks
Qi Peng, Wenlin Liu, Ruoxi Qin +3
Adversarial attacks are considered the intrinsic vulnerability of CNNs. Defense strategies designed for attacks have been stuck in the adversarial attack-defense arms race, reflect…
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
Pengfei Xie, Linyuan Wang, Ruoxi Qin +4
Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt…
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
Ruoxi Qin, Linyuan Wang, Xingyuan Chen +2
Deep neural networks have been shown to suffer from critical vulnerabilities under adversarial attacks. This phenomenon stimulated the creation of different attack and defense stra…
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
Lingyun Jiang, Kai Qiao, Ruoxi Qin +4
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect resul…