132 citations · 443 across the 43 of their papers we have counts for
8 papers · 1 filter
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…