239 citations · 422 across the 13 of their papers we have counts for
6 papers · 1 filter
Towards Adversarially Robust Continual Learning
Tao Bai, Chen Chen, Lingjuan Lyu +2
Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual…
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method
Tao Bai, Jun Zhao, Jinlin Zhu +4
Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversari…
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…