26 citations · 49 across the 7 of their papers we have counts for
7 papers
Adaptive Task Offloading for Space Missions: A State-Graph-Based Approach
Jiaqi Cao, Shengli Zhang, Mingzhe Wang +3
Advances in space exploration have led to an explosion of tasks. Conventionally, these tasks are offloaded to ground servers for enhanced computing capability, or to adjacent low-e…
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
Haidong Xie, Jia Tan, Xiaoying Zhang +5
Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficien…
Adversarial Jamming for a More Effective Constellation Attack
Haidong Xie, Yizhou Xu, Yuanqing Chen +4
The common jamming mode in wireless communication is band barrage jamming, which is controllable and difficult to resist. Although this method is simple to implement, it is obvious…
Secure Blockchain Platform for Industrial IoT with Trusted Computing Hardware
Qing Yang, Hao Wang, Xiaoxiao Wu +3
As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate industrial IoT (IIoT) applications. However, implementing a blo…
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
Nan Ji, YanFei Feng, Haidong Xie +2
The security of object detection systems has attracted increasing attention, especially when facing adversarial patch attacks. Since patch attacks change the pixels in a restricted…
Blind Adversarial Training: Balance Accuracy and Robustness
Haidong Xie, Xueshuang Xiang, Naijin Liu +1
Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped int…
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Haidong Xie, Lixin Qian, Xueshuang Xiang +1
With the growth of interest in the attack and defense of deep neural networks, researchers are focusing more on the robustness of applying them to devices with limited memory. Thus…