43 citations · 56 across the 5 of their papers we have counts for
5 papers
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…
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…
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices
Tao Bai, Jinqi Luo, Jun Zhao
Deep learning based image recognition systems have been widely deployed on mobile devices in today's world. In recent studies, however, deep learning models are shown vulnerable to…
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…