132 citations · 443 across the 43 of their papers we have counts for
43 papers
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery
Jinyin Chen, Jie Ge, Shilian Zheng +5
A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorte…
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space
Haibin Zheng, Jinyin Chen, Haibo Jin
Deep neural networks (DNNs) have demonstrated their outperformance in various software systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, i…
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking
Ruoxi Chen, Haibo Jin, Jinyin Chen +1
Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to character…
Edge Deep Learning Model Protection via Neuron Authorization
Jinyin Chen, Haibin Zheng, Tao Liu +4
With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device mod…
FedRight: An Effective Model Copyright Protection for Federated Learning
Jinyin Chen, Mingjun Li, Haibin Zheng
Federated learning (FL), an effective distributed machine learning framework, implements model training and meanwhile protects local data privacy. It has been applied to a broad va…
ActGraph: Prioritization of Test Cases Based on Deep Neural Network Activation Graph
Jinyin Chen, Jie Ge, Haibin Zheng
Widespread applications of deep neural networks (DNNs) benefit from DNN testing to guarantee their quality. In the DNN testing, numerous test cases are fed into the model to explor…
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Haibin Zheng, Haiyang Xiong, Jinyin Chen +2
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas, such as biological gene prediction, social recommendation, etc. Recen…
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
Haibin Zheng, Haiyang Xiong, Haonan Ma +2
Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real-world. By facilitating labeled links of the graph as the training data, n…
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection
Jinyin Chen, Chengyu Jia, Haibin Zheng +2
The proliferation of fake news and its serious negative social influence push fake news detection methods to become necessary tools for web managers. Meanwhile, the multi-media nat…
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency
Jinyin Chen, Mingjun Li, Tao Liu +3
Federated learning (FL) is a distributed machine learning approach where multiple clients collaboratively train a joint model without exchanging their data. Despite FL's unpreceden…
Improving robustness of language models from a geometry-aware perspective
Bin Zhu, Zhaoquan Gu, Le Wang +2
Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness. However, we observe th…
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning
Jinyin Chen, Shulong Hu, Haibin Zheng +2
Penetration testing (PT) is an efficient network testing and vulnerability mining tool by simulating a hacker's attack for valuable information applied in some areas. Compared with…
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Haibo Jin, Ruoxi Chen, Haibin Zheng +4
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequen…
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…