Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Zheng Haibin"'
Publikováno v:
网络与信息安全学报, Vol 10, Pp 1-21 (2024)
Deep learning models are misled into making false predictions by adversarial attacks that implant tiny perturbations into the original input, which are imperceptible to the human eye. This poses a huge security threat to computer vision systems that
Externí odkaz:
https://doaj.org/article/d771c9c4660c49bca0e37e2e616f18e2
Autor:
Chen Xiaoqin, Zheng Haibin
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In this study, the intrinsic model of Shape Memory Alloys (SMA) is employed to facilitate a comprehensive analysis of the phase transition process, encapsulating the evolution of stress and strain across varying temperatures. The martensite volume fr
Externí odkaz:
https://doaj.org/article/6fe0c548a9e34de9aa7eb90944453705
Graph has become increasingly integral to the advancement of recommendation systems, particularly with the fast development of graph neural network(GNN). By exploring the virtue of rich node features and link information, GNN is designed to provide p
Externí odkaz:
http://arxiv.org/abs/2411.03364
Recent research has constructed successful graph reconstruction attack (GRA) on GFL. But these attacks are still challenged in aspects of effectiveness and stealth. To address the issues, we propose the first Data Manipulation aided Reconstruction at
Externí odkaz:
http://arxiv.org/abs/2411.02866
Since DNN is vulnerable to carefully crafted adversarial examples, adversarial attack on LiDAR sensors have been extensively studied. We introduce a robust black-box attack dubbed LiDAttack. It utilizes a genetic algorithm with a simulated annealing
Externí odkaz:
http://arxiv.org/abs/2411.01889
Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit the performance of GNN. Vertical federated learning (VFL), an emerging
Externí odkaz:
http://arxiv.org/abs/2411.02809
Benefiting from well-trained deep neural networks (DNNs), model compression have captured special attention for computing resource limited equipment, especially edge devices. Knowledge distillation (KD) is one of the widely used compression technique
Externí odkaz:
http://arxiv.org/abs/2406.03409
Autor:
Chen, Jinyin, Ge, Jie, Zheng, Shilian, Ye, Linhui, Zheng, Haibin, Shen, Weiguo, Yue, Keqiang, Yang, Xiaoniu
A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance
Externí odkaz:
http://arxiv.org/abs/2309.16706
Deep neural networks (DNNs) have demonstrated their outperformance in various software systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, it is crucial to identify the misbehavior of DNN-based software and imp
Externí odkaz:
http://arxiv.org/abs/2307.09375
Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to characterize the feature uniqueness of adversarial examples, or to distinguish
Externí odkaz:
http://arxiv.org/abs/2303.18131