Qi Xuan

Can Adversarial Network Attack be Defended?

2019 · arXiv (Cornell University)

Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since carefully crafted adversarial …

Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition

2022 · arXiv (Cornell University)

With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, …

EMD and VMD Empowered Deep Learning for Radio Modulation Recognition

2022 · IEEE Transactions on Cognitive Communications and Networking

Deep learning has been widely exploited in radio modulation recognition in recent years. In this paper, we exploit empirical mode decomposition (EMD) and variational mode decomposition (VMD) in deep learning-based radio modulation recognition. The received …

A universal adversarial perturbations generation method based on feature aggregation

2024

Universal Adversarial Perturbations (UAP) is a sample-independent adversarial attack that can be added to all natural samples to change most of their predictive labels. Aiming at the problems of the existing universal adversarial attack methods …