Jin Hu

I am currently a PhD student (2023-) at Zhongguancun Laboratory (ZGCLab) and State Key Laboratory of Complex & Critical Software Environment, Beihang University, advised by Prof. Xianglong Liu, Prof. Ke Xu and Dr. Jiakai Wang. My research focuses on Trustworthy AI: understanding how AI systems fail and developing methods to evaluate and improve their reliability. I develop generative methods to expose model vulnerabilities and study the internal computations behind model behavior. Our work connects practical failure cases with generative modeling and studies of internal computation.

News

Sep 28, 2026	One first-author paper submitted to ICLR 2027.
Jul 31, 2026	A co-authored paper has been accepted for an oral presentation (2.8%) at PRCV 2026.
Jul 25, 2026	MiuGS has been accepted at ACM Multimedia 2026. The work studies a shared 3D representation for adversarial objects observed by cameras and LiDAR. The project repository is available.
Jun 04, 2026	We co-organized the 6th AdvML Workshop on Safety of Vision-Language Agents at CVPR 2026, held on June 4 in Denver. The workshop brought together research on reliable visual and multimodal systems.
Apr 04, 2026	Two co-authored papers on the 3D adversarial attacks submitted to ACM Multimedia 2026.

Publications

ACM MM 2026

MiuGS: Modality-Integrated Unified Gaussian Representation for Physical Adversarial Object Generation in 3D Detection

Zhengquan Sun, Jin Hu, Tianyuan Zhang, Fei Xiansong, Xiaoqi Jiang, Jiakai Wang, and Xianglong Liu

In Proceedings of the 34th ACM International Conference on Multimedia. Accepted; manuscript not yet public , 2026

Bib Code

@inproceedings{sun2026miugs,
  title = {MiuGS: Modality-Integrated Unified Gaussian Representation for Physical Adversarial Object Generation in 3D Detection},
  author = {Sun, Zhengquan and Hu, Jin and Zhang, Tianyuan and Xiansong, Fei and Jiang, Xiaoqi and Wang, Jiakai and Liu, Xianglong},
  booktitle = {Proceedings of the 34th ACM International Conference on Multimedia},
  year = {2026},
}

TPAMI 2026

DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time

Jin Hu, Xianglong Liu^*, Jiakai Wang, Junkai Zhang, Xianqi Yang, Haotong Qin, Yuqing Ma, and Ke Xu

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

DOI Bib HTML Code

@article{hu2024dynamicpae,
  title = {DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time},
  author = {Hu, Jin and Liu, Xianglong and Wang, Jiakai and Zhang, Junkai and Yang, Xianqi and Qin, Haotong and Ma, Yuqing and Xu, Ke},
  selected = true,
  year = {2026},
  volume = {48},
  number = {3},
  pages = {2413-2430},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  doi = {10.1109/TPAMI.2025.3626068},
}

NeurIPS 2025

Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction

Jin Hu, Jiakai Wang^*, Linna Jing, Haolin Li, Haodong Liu, Haotong Qin, Aishan Liu, Ke Xu, and Xianglong Liu

In Neural Information Processing Systems (NeurIPS), 2025

arXiv Bib Blog Code Website

@inproceedings{Hu2025exploring,
  title = {Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction},
  author = {Hu, Jin and Wang, Jiakai and Jing, Linna and Li, Haolin and Liu, Haodong and Qin, Haotong and Liu, Aishan and Xu, Ke and Liu, Xianglong},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  selected = true,
  url = {https://nips.cc/virtual/2025/poster/116399},
}

Survey Project

Adversarial Examples in the Physical World: A Survey

Jiakai Wang, Xianglong Liu^*, Jin Hu, Donghua Wang, Siyang Wu, Tingsong Jiang, Yuanfang Guo, Aishan Liu, and Jiantao Zhou

CoRR, 2023

DOI arXiv Bib Website

@article{wang2023AdversarialSurvey,
  title = {Adversarial Examples in the Physical World: {A} Survey},
  author = {Wang, Jiakai and Liu, Xianglong and Hu, Jin and Wang, Donghua and Wu, Siyang and Jiang, Tingsong and Guo, Yuanfang and Liu, Aishan and Zhou, Jiantao},
  journal = {CoRR},
  volume = {abs/2311.01473},
  year = {2023},
  url = {https://doi.org/10.48550/arXiv.2311.01473},
  doi = {10.48550/ARXIV.2311.01473},
  eprinttype = {arXiv},
  eprint = {2311.01473},
  timestamp = {Tue, 07 Nov 2023 18:17:14 +0100},
  biburl = {https://dblp.org/rec/journals/corr/abs-2311-01473.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  selected = true,
}

ICASSP 2025

Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Yujun Zhang, Yanqu Chen, Jiakai Wang^*, Jin Hu, Renshuai Tao, and Xianglong Liu

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025

Bib HTML

@inproceedings{zhang2025generating,
  title = {Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring},
  author = {Zhang, Yujun and Chen, Yanqu and Wang, Jiakai and Hu, Jin and Tao, Renshuai and Liu, Xianglong},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {1--5},
  year = {2025},
  organization = {IEEE},
  selected = true,
}

Technical report

Evaluating the practicality of learned image compression

Hongjiu Yu, Qiancheng Sun, Jin Hu, Xingyuan Xue, Jixiang Luo, Dailan He, Yilong Li, Pengbo Wang, Yuanyuan Wang, Yaxu Dai, and others

arXiv preprint arXiv:2207.14524, 2022

arXiv Bib

@article{yu2022evaluating,
  title = {Evaluating the practicality of learned image compression},
  author = {Yu, Hongjiu and Sun, Qiancheng and Hu, Jin and Xue, Xingyuan and Luo, Jixiang and He, Dailan and Li, Yilong and Wang, Pengbo and Wang, Yuanyuan and Dai, Yaxu and others},
  journal = {arXiv preprint arXiv:2207.14524},
  selected = true,
  year = {2022}
}

Service

Reviewer: ICLR, NeurIPS, ACM MM, TNNLS, and ICASSP.