top of page



过去几年,我们在软件工程和安全领域的国际旗舰会议和期刊上发表了诸多关于人工智能检测的论文,例如ICSE、USENIX Security、CAV、TSE和FSE。

这些论文涵盖了可信赖人工智能的各个领域,包括鲁棒性、公平性、安全性和可解释性等。此外,我们还获得了两项 ACM SIGSOFT 杰出论文奖(ICSE 2018、ICSE 2020)和一项 ACM SIGSOFT 研究亮点奖(2020)。

Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors,CAV,2024 年 7 月

QuoTe: Quality-oriented Testing for Deep Learning Systems,TOSEM,2023 年 2 月

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems,IJCIP,2021 年 9 月

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing,ICSE,2019年5月
Towards Optimal Concolic Testing,ICSE,2018年5月



TestSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination,TOSEM,2023年4月

Adaptive Fairness Improvement based Causality Analysis,FSE,2022年11月

Probabilistic Verification of Neural Networks Against Group Fairness,FM,2021年11

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling,TSE,2021 年 8 月

White-box Fairness Testing through Adversarial Sampling,ICSE,2020年6月


Neural Network Semantic Backdoor Detection and Mitigation: A Causality-Based Approach,USENIX Security,2024年8

Verifying Neural Networks Against Backdoor Attacks,CAV,2022年8

Causality-based Neural Network Repair,ICSE,2022 年 7 月


Semantic-based Neural Network Repair,ISSTA,2023年7月


Which Neural Network Makes More Explainable Decisions? An Approach towards Measuring Explainability,ASE-J,2022 年 11 月

ExAIs: Executable AI Semantics,ICSE,2022年7月

Towards Interpreting Recurrent Neural Network through Probabilistic Abstraction,ASE,2020 年 9 月


bottom of page