5 papers
Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +4
The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulatio…
A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +1
Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that e…
Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulati…
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applyi…
Countermeasures Against Adversarial Examples in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +2
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vuln…