239 citations · 422 across the 13 of their papers we have counts for
14 papers
On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing
Tao Bai, Xingjian Tian, Yonghao Xu +1
The use of pretrained models from general computer vision tasks is widespread in remote sensing, significantly reducing training costs and improving performance. However, this prac…
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…
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Yonghao Xu, Tao Bai, Weikang Yu +3
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-bas…
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…
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.…
Generating Adversarial yet Inconspicuous Patches with a Single Image
Jinqi Luo, Tao Bai, Jun Zhao
Deep neural networks have been shown vulnerable toadversarial patches, where exotic patterns can resultin models wrong prediction. Nevertheless, existing ap-proaches to adversarial…
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…
Conditional Analysis for Key-Value Data with Local Differential Privacy
Lin Sun, Jun Zhao, Xiaojun Ye +3
Local differential privacy (LDP) has been deemed as the de facto measure for privacy-preserving distributed data collection and analysis. Recently, researchers have extended LDP to…
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Jingtuo Liu, Yafeng Deng, Tao Bai +2
Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatical…