43 citations · 84 across the 6 of their papers we have counts for
7 papers
Adversarial Purification through Representation Disentanglement
Tao Bai, Jun Zhao, Lanqing Guo +1
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment. Combined with the idea of advers…
Recent Advances in Adversarial Training for Adversarial Robustness
Tao Bai, Jinqi Luo, Jun Zhao +2
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training…
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
Tao Bai, Jinqi Luo, Jun Zhao
Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN). Imperceptible perturbations applied on natural samples can lead DNN-based c…
Feature Distillation With Guided Adversarial Contrastive Learning
Tao Bai, Jinnan Chen, Jun Zhao +3
Deep learning models are shown to be vulnerable to adversarial examples. Though adversarial training can enhance model robustness, typical approaches are computationally expensive.…
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Tao Bai, Jun Zhao, Jinlin Zhu +4
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies hav…
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Jun Zhao, Teng Wang, Tao Bai +7
Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…