132 citations · 443 across the 43 of their papers we have counts for
13 papers · 1 filter
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…
Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence
Ruoxi Chen, Haibo Jin, Haibin Zheng +2
The vulnerabilities of deep learning models towards adversarial attacks have attracted increasing attention, especially when models are deployed in security-critical domains. Numer…
TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors
Jinyin Chen, Haiyang Xiong, Dunjie Zhang +2
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target…
Understanding the Dynamics of DNNs Using Graph Modularity
Yao Lu, Wen Yang, Yunzhe Zhang +5
There are good arguments to support the claim that deep neural networks (DNNs) capture better feature representations than the previous hand-crafted feature engineering, which lead…
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction
Jinyin Chen, Haiyang Xiong, Haibin Zheng +3
Dynamic link prediction (DLP) makes graph prediction based on historical information. Since most DLP methods are highly dependent on the training data to achieve satisfying predict…
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
Jinyin Chen, Guohan Huang, Haibin Zheng +3
Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large scale private data collected from user-side, GNN may not be able to refl…
Blockchain Phishing Scam Detection via Multi-channel Graph Classification
Dunjie Zhang, Jinyin Chen
With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods wil…
EGC2: Enhanced Graph Classification with Easy Graph Compression
Jinyin Chen, Haiyang Xiong, Haibin Zhenga +4
Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complex…
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks
Jinyin Chen, Ruoxi Chen, Haibin Zheng +3
Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully crafted adversari…
MASS: Multi-task Anthropomorphic Speech Synthesis Framework
Jinyin Chen, Linhui Ye, Zhaoyan Ming
Text-to-Speech (TTS) synthesis plays an important role in human-computer interaction. Currently, most TTS technologies focus on the naturalness of speech, namely,making the speeche…
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
Jinyin Chen, Xiang Lin, Dunjie Zhang +4
Deep learning is effective in graph analysis. It is widely applied in many related areas, such as link prediction, node classification, community detection, and graph classificatio…
DeepPoison: Feature Transfer Based Stealthy Poisoning Attack
Jinyin Chen, Longyuan Zhang, Haibin Zheng +2
Deep neural networks are susceptible to poisoning attacks by purposely polluted training data with specific triggers. As existing episodes mainly focused on attack success rate wit…
GraphAttacker: A General Multi-Task GraphAttack Framework
Jinyin Chen, Dunjie Zhang, Zhaoyan Ming +3
Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. The competition between attack and defense methods also enhan…