Publications

An up-to-date list is available on Google Scholar.

2025

2025

1. arXiv

   

   BoT: Breaking Long Thought Processes of o1-like Large Language Models through Backdoor Attack

   Zihao Zhu, Hongbao Zhang, Mingda Zhang, Ruotong Wang , Guanzong Wu, Xu Ke, and Baoyuan Wu

   arXiv preprint, 2025

   Bib PDF Code Website

   @article{zhu2025bot,
     title = {BoT: Breaking Long Thought Processes of o1-like Large Language Models through Backdoor Attack},
     author = {Zhu, Zihao and Zhang, Hongbao and Zhang, Mingda and Wang, Ruotong and Wu, Guanzong and Ke, Xu and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2025},
   }

2024

2024

1. arXiv

   

   HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing

   Zihao Zhu, Hongbao Zhang , Guanzong Wu, Siwei Lyu, and Baoyuan Wu

   arXiv preprint, 2024

   Bib PDF

   @article{zhu2024hmgie,
     title = {HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing},
     author = {Zhu, Zihao and Zhang, Hongbao and Wu, Guanzong and Lyu, Siwei and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2024},
   }

2. arXiv

   

   Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization

   Mingda Zhang, Mingli Zhu, Zihao Zhu, and Baoyuan Wu

   arXiv preprint, 2024

   Bib PDF

   @article{zhang2024reliable,
     title = {Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization},
     author = {Zhang, Mingda and Zhu, Mingli and Zhu, Zihao and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2024},
   }

3. arXiv

   

   EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents

   Zihao Zhu , Bingzhe Wu, Zhengyou Zhang, Lei Han, Qingshan Liu, and Baoyuan Wu

   arXiv preprint, 2024

   Bib PDF Code

   @article{zhu2024earbench,
     title = {EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents},
     author = {Zhu, Zihao and Wu, Bingzhe and Zhang, Zhengyou and Han, Lei and Liu, Qingshan and Wu, Baoyuan},
     year = {2024},
     journal = {arXiv preprint},
   }

4. arXiv

   

   BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning

   Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Mingli Zhu, Ruotong Wang, Li Liu, and Chao Shen

   arXiv preprint, 2024

   Bib PDF

   @article{wu2024backdoorbench,
     title = {BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning},
     author = {Wu, Baoyuan and Chen, Hongrui and Zhang, Mingda and Zhu, Zihao and Wei, Shaokui and Yuan, Danni and Zhu, Mingli and Wang, Ruotong and Liu, Li and Shen, Chao},
     journal = {arXiv preprint},
     year = {2024},
   }

5. arXiv

   

   Enhanced Few-Shot Class-Incremental Learning via Ensemble Models

   Mingli Zhu, Zihao Zhu, Sihong Chen, Chen Chen, and Baoyuan Wu

   arXiv preprint, 2024

   Bib PDF

   @article{zhu2024enhanced,
     title = {Enhanced Few-Shot Class-Incremental Learning via Ensemble Models},
     author = {Zhu, Mingli and Zhu, Zihao and Chen, Sihong and Chen, Chen and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2024},
   }

6. ICLR

   

   VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models

   Zihao Zhu, Mingda Zhang, Shaokui Wei , Bingzhe Wu, and Baoyuan Wu

   In International Conference on Learning Representations, 2024

   Bib PDF Code Website

   @inproceedings{zhu2024vdc,
     title = {VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models},
     author = {Zhu, Zihao and Zhang, Mingda and Wei, Shaokui and Wu, Bingzhe and Wu, Baoyuan},
     booktitle = {International Conference on Learning Representations},
     year = {2024},
   }

7. arXiv

   

   Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers

   Ruotong Wang, Hongrui Chen, Zihao Zhu, Li Liu, and Baoyuan Wu

   arXiv preprint, 2024

   Bib PDF

   @article{wang2024versatile,
     title = {Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers},
     author = {Wang, Ruotong and Chen, Hongrui and Zhu, Zihao and Liu, Li and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2024},
   }

8. AAAI

   

   Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

   Longkang Li, Siyuan Liang, Zihao Zhu, Xiaochun Cao, Chris Ding, Hongyuan Zha, and Baoyuan Wu

   In AAAI Conference on Artificial Intelligence, 2024

   Bib PDF

   @inproceedings{li2024learning,
     title = {Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning},
     author = {Li, Longkang and Liang, Siyuan and Zhu, Zihao and Cao, Xiaochun and Ding, Chris and Zha, Hongyuan and Wu, Baoyuan},
     booktitle = {AAAI Conference on Artificial Intelligence},
     year = {2024},
   }

2023

2023

1. arXiv

   

   BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attacks

   Meixi Zheng, Xuanchen Yan, Zihao Zhu, Hongrui Chen, and Baoyuan Wu

   arXiv preprint, 2023

   Bib PDF

   @article{zheng2023blackboxbench,
     title = {BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attacks},
     author = {Zheng, Meixi and Yan, Xuanchen and Zhu, Zihao and Chen, Hongrui and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2023},
   }

2. arXiv

   

   Defenses in adversarial machine learning: A survey

   Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng, Zihao Zhu, Mingda Zhang, Hongrui Chen, Danni Yuan, Li Liu, and Qingshan Liu

   arXiv preprint, 2023

   Bib PDF

   @article{wu2023defenses,
     title = {Defenses in adversarial machine learning: A survey},
     author = {Wu, Baoyuan and Wei, Shaokui and Zhu, Mingli and Zheng, Meixi and Zhu, Zihao and Zhang, Mingda and Chen, Hongrui and Yuan, Danni and Liu, Li and Liu, Qingshan},
     journal = {arXiv preprint},
     year = {2023},
   }

3. arXiv

   

   Boosting backdoor attack with a learnable poisoning sample selection strategy

   Zihao Zhu, Mingda Zhang, Shaokui Wei, Li Shen, Yanbo Fan, and Baoyuan Wu

   arXiv preprint, 2023

   Bib PDF

   @article{zhu2023boosting,
     title = {Boosting backdoor attack with a learnable poisoning sample selection strategy},
     author = {Zhu, Zihao and Zhang, Mingda and Wei, Shaokui and Shen, Li and Fan, Yanbo and Wu, Baoyuan},
     journal = {arXiv preprint},
     year = {2023},
   }

4. arXiv

   

   Attacks in adversarial machine learning: A systematic survey from the life-cycle perspective

   Baoyuan Wu, Zihao Zhu, Li Liu, Qingshan Liu, Zhaofeng He, and Siwei Lyu

   arXiv preprint, 2023

   Bib PDF

   @article{wu2023attacks,
     title = {Attacks in adversarial machine learning: A systematic survey from the life-cycle perspective},
     author = {Wu, Baoyuan and Zhu, Zihao and Liu, Li and Liu, Qingshan and He, Zhaofeng and Lyu, Siwei},
     journal = {arXiv preprint},
     year = {2023},
   }

2022

2022

1. NeurIPS

   

   BackdoorBench: A Comprehensive Benchmark of Backdoor Learning

   Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, and Hongyuan Zha

   In Advances in Neural Information Processing Systems, 2022

   Bib PDF Code Website

   @inproceedings{wu2022backdoorbench,
     title = {BackdoorBench: A Comprehensive Benchmark of Backdoor Learning},
     author = {Wu, Baoyuan and Chen, Hongrui and Zhang, Mingda and Zhu, Zihao and Wei, Shaokui and Yuan, Danni and Zha, Hongyuan},
     booktitle = {Advances in Neural Information Processing Systems},
     year = {2022},
   }

2. ICASSP

   

   From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering

   Zihao Zhu

   In IEEE International Conference on Acoustics, Speech and Signal Processing, 2022

   Bib PDF

   @inproceedings{zhu2022shallow,
     title = {From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering},
     author = {Zhu, Zihao},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing},
     year = {2022},
   }

2021

2021

1. ICASSP

   

   MCR-NET: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension

   Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Zihao Zhu, and Yajing Sun

   In IEEE International Conference on Acoustics, Speech and Signal Processing, 2021

   Bib PDF

   @inproceedings{peng2021mcr,
     title = {MCR-NET: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension},
     author = {Peng, Wei and Hu, Yue and Xing, Luxi and Xie, Yuqiang and Zhu, Zihao and Sun, Yajing},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing},
     year = {2021},
   }

2020

2020

1. PR

   

   Cross-Modal Knowledge Reasoning for Knowledge-based Visual Question Answering

   Jing Yu, Zihao Zhu, Yujing Wang, Weifeng Zhang, Yue Hu, and Jianlong Tan

   Pattern Recognition, 2020

   Bib PDF

   @article{yu2020cross,
     title = {Cross-Modal Knowledge Reasoning for Knowledge-based Visual Question Answering},
     author = {Yu, Jing and Zhu, Zihao and Wang, Yujing and Zhang, Weifeng and Hu, Yue and Tan, Jianlong},
     journal = {Pattern Recognition},
     year = {2020},
   }

2. IJCAI

   

   Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

   Zihao Zhu, Jing Yu, Yujing Wang, Yajing Sun, Yue Hu , and Qi Wu

   In Proceedings of the International Joint Conference on Artificial Intelligence, 2020

   Bib PDF

   @inproceedings{zhu2020mucko,
     title = {Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering},
     author = {Zhu, Zihao and Yu, Jing and Wang, Yujing and Sun, Yajing and Hu, Yue and Wu, Qi},
     booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
     year = {2020},
   }

3. IJCAI

   

   DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses

   Xiaoze Jiang, Jing Yu, Zengchang Qin, Zihao Zhu , and Qi Wu

   In Proceedings of the International Joint Conference on Artificial Intelligence, 2020

   Bib PDF

   @inproceedings{jiang2020dam,
     title = {DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses},
     author = {Jiang, Xiaoze and Yu, Jing and Qin, Zengchang and Zhu, Zihao and Wu, Qi},
     booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
     year = {2020},
   }