no code implementations • 20 Jan 2022 • Haidong Xie, Jia Tan, Xiaoying Zhang, Nan Ji, Haihua Liao, Zuguo Yu, Xueshuang Xiang, Naijin Liu
This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform.
no code implementations • 16 Mar 2021 • Nan Ji, YanFei Feng, Haidong Xie, Xueshuang Xiang, Naijin Liu
To improve the ability of Ad-YOLO to detect variety patches, we first use an adversarial training process to develop a patch dataset based on the Inria dataset, which we name Inria-Patch.
1 code implementation • 10 Apr 2020 • Haidong Xie, Lixin Qian, Xueshuang Xiang, Naijin Liu
Furthermore, to better balance the AER, we propose an approach called blind adversarial pruning (BAP), which introduces the idea of blind adversarial training into the gradual pruning process.
1 code implementation • 10 Apr 2020 • Haidong Xie, Xueshuang Xiang, Naijin Liu, Bin Dong
The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model.
1 code implementation • 17 Jan 2017 • Jing Chen, Song Cheng, Haidong Xie, Lei Wang, Tao Xiang
Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures.