19 citations · 33 across the 7 of their papers we have counts for
7 papers
A semantic backdoor attack against Graph Convolutional Networks
Jiazhu Dai, Zhipeng Xiong
Graph convolutional networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks. However, recent research has shown that GCNs are vul…
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training
Jiazhu Dai, Siwei Xiong
Capsule networks (CapsNets) are new neural networks that classify images based on the spatial relationships of features. By analyzing the pose of features and their relative positi…
A Targeted Universal Attack on Graph Convolutional Network
Jiazhu Dai, Weifeng Zhu, Xiangfeng Luo
Graph-structured data exist in numerous applications in real life. As a state-of-the-art graph neural network, the graph convolutional network (GCN) plays an important role in proc…
An Evasion Attack against Stacked Capsule Autoencoder
Jiazhu Dai, Siwei Xiong
Capsule network is a type of neural network that uses the spatial relationship between features to classify images. By capturing the poses and relative positions between features,…
Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification
Chuanshuai Chen, Jiazhu Dai
It has been proved that deep neural networks are facing a new threat called backdoor attacks, where the adversary can inject backdoors into the neural network model through poisoni…
Fast-UAP: An Algorithm for Speeding up Universal Adversarial Perturbation Generation with Orientation of Perturbation Vectors
Jiazhu Dai, Le Shu
Convolutional neural networks (CNN) have become one of the most popular machine learning tools and are being applied in various tasks, however, CNN models are vulnerable to univers…
A backdoor attack against LSTM-based text classification systems
Jiazhu Dai, Chuanshuai Chen
With the widespread use of deep learning system in many applications, the adversary has strong incentive to explore vulnerabilities of deep neural networks and manipulate them. Bac…