Shu Hu 胡暑

Shu Hu 

Ph.D.
Assistant Professor, Department of Computer and Information Technology
Director, Purdue Machine Learning and Media Forensics (M2) Lab
Purdue University
Email: hu968 [at] purdue (dot) edu
Office: ET 201K, 799 W. Michigan Street, Indianapolis, IN, 46202
Google Scholar

About Me

I am an assistant professor in the Department of Computer and Information Technology and the Director of the Purdue Machine Learning and Media Forensics (M2) Lab at Purdue University, USA.

I was an assistant professor at Indiana University-Purdue University Indianapolis from 2023 to 2024. Before that, I was a Post-Doctoral Fellow at the Heinz College of Carnegie Mellon University from 2022 to 2023, where I worked with Prof. George H. Chen. I received my Ph.D. degree in Computer Science and Engineering from University at Buffalo (UBuffalo), SUNY in 2022 under the advising of Prof. Siwei Lyu. I spend my first and second year of Ph.D. at University at Albany (UAlbany), SUNY. During the first year of my Ph.D., I was working with Prof. Feng Chen. I received my Master of Arts (Mathematics) degree from University at Albany, SUNY in 2020 under the advising of Prof. Yiming Ying and I also received my Master of Engineering degree from University of Science and Technology of China (USTC) in 2016 under the advising of Prof. En-Hong Chen. I was previously a visiting student (2014-2015) at University of South Australia (UniSA), where I worked with Prof. Jiuyong Li and Dr. Thuc Duy Le.

I am the recipient of the National AI Research Resource (NAIRR) Pilot award (2024), the National Science Foundation CRII Award (2024), the Machine Intelligence Research Outstanding Reviewer Award (2023), SUNY Buffalo's CSE Best PhD Dissertation Award (2022), SUNY Buffalo's Honorable Mention Award of Agrusa CSE Student Innovation Competition (2021), and the first place of the graduate poster competition (2020) in SUNY Buffalo.

My current research interests include machine learning, media forensics, and computer vision.

News

  • (5/20/2024) Five papers accepted by MIPR 2024.

  • (5/13/2024) One paper accepted by AVSS 2024 and Four papers accepted by AIMS Workshop 2024.

  • (5/8/2024) I was interviewed by Science Magazine on my NAIRR award [Link].

  • (5/6/2024) I received the NSF-led National AI Research Resource (NAIRR) Pilot award to support my lab's deepfake detection research. Thanks to NSF and DOE! [NSF News][Award][Purdue News]

  • (4/24/2024) I received the NSF CRII Award ($175,000) as a sole PI. Thanks to NSF!

  • (3/15/2024) Four papers accepted by IJCNN 2024.

  • (2/26/2024) Congratulations to Ph.D. student Li Lin for having her paper accepted by CVPR 2024 (Acceptance rate 23.6% of 11,532 submissions) as the first author, an impressive achievement just three months after joining the group.

  • (2/16/2024) One paper accepted by Electronics Journal 2024.

  • (2/2/2024) One paper accepted by ISBI 2024.

  • (1/23/2024) Our survey 'Detecting Multimedia Generated by Large AI Models: A Survey' is avaiable on [ArXiv].

  • (12/13/2023) One paper accepted by ICASSP 2024 (Acceptance rate 45% of 5796 submissions).

  • (11/24/2023) I am invited to serve The 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) 2024 as a Organizing Committee Member.

  • (10/24/2023) One paper accepted by WACV 2024 (Acceptance rate 41.7% of 2041 submissions) [PDF][Code].

  • (10/16/2023) I am invited to serve The 7th IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024 as a Program Chair.

  • (09/10/2023) One paper accepted by ACML 2023 (Acceptance rate 35% of 333 submissions) [PDF][Code].

  • (08/12/2023) Our patent is published [Link].

  • (07/15/2023) Our GAN Face Detection survey is accepted by ECAI 2023 (Acceptance rate 24% of 1631 submissions). Please check it [HERE]

  • (07/05/2023) Our survey is accepted by TPAMI Journal 2023. Please check it [HERE]

  • (06/21/2023) One paper accepted by IROS 2023 (Acceptance rate 43.3% of 2760 submissions).

  • (06/17/2023) One paper accepted by MIPR 2023 (Invited Papers Track).

  • (04/19/2023) One paper accepted by IJCAI 2023 (Acceptance rate 15% of 4566 submissions).

  • (04/03/2023) One paper accepted by NTIRE Workshop at CVPR 2023 (as a participating team).

  • (04/03/2023) One paper accepted by Media Forensics Workshop at CVPR 2023.

  • (04/01/2023) One paper accepted by IEEE Access Journal.

  • (02/24/2023) One paper accepted by Frontiers in Physics Journal.

  • (01/16/2023) I received the Outstanding Reviewer Award from Machine Intelligence Research.[Award] [Plaque]

  • (12/07/2022) I received the CSE Best PhD Dissertation Award with a prize of $500 from University at Buffalo, SUNY.[Award] [Plaque and Check]

  • (10/22/2022) One paper accepted by Machine Learning for Health (ML4H 2022) symposium.

  • (07/18/2022) Our survey is avaiable on ArXiv. Please check it [HERE]. This is the first comprehensive survey on Rank-based Decomposable Losses in Machine Learning. Most importantly, we introduce a new set function, aggregator, to formulate aggregate loss and individual loss. We hope this survey can help researchers to design loss more flexibly in the future.

  • (06/03/2022) One paper accepted by Pattern Recognition Journal.

  • (05/15/2022) One paper accepted by the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).

  • (05/09/2022) One demo paper accepted by IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR 2022).

  • (04/22/2022) I have passed my Ph.D. dissertation defense.

  • (04/15/2022) One paper accepted by Autonomous Driving Workshop at CVPR 2022.

  • (04/03/2022) One paper accepted by Journal of Machine Learning Research (JMLR).

  • (03/06/2022) One paper accepted by IEEE International Conference on Multimedia and Expo (ICME 2022).

  • (02/25/2022) One paper accepted by IEEE Access Journal.

  • (01/21/2022) One paper accepted by ICASSP 2022 [PDF]

  • (01/13/2022) One paper accepted by Computer Networks Journal [PDF]

  • (12/10/2021) I received the Honorable Mention of Agrusa CSE Student Innovation Competition 2021, University at Buffalo, SUNY. [Award]

  • (12/01/2021) One paper accepted by AAAI Conference on Artificial Intelligence (AAAI 2022).

  • (08/19/2021) I have passed my doctoral dissertation proposal defense.

  • (07/22/2021) One paper accepted by the International Conference on Computer Vision (ICCV 2021).

  • (06/21/2021) One paper accepted by the 2021 International Joint Conference on Biometrics (IJCB 2021).

  • (05/12/2021) I will join Robert Bosch LLC (Sunnyvale, CA) as a Deep Learning Research Intern (June 2021 - Aug 2021).

  • (03/15/2021) Our work on detecting GAN generated images using corneal reflections is reported by UB News. [Link]

  • (01/29/2021) One paper accepted by ICASSP 2021.

  • (12/11/2020) I got the first place in the graduate poster competition from the CSE department, University at Buffalo, SUNY. [Award]

  • (10/13/2020) Our work ''Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights'' has been mentioned by Financial Times and usa-vision

Preprints

  • AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark [PDF][Code]
    Li Lin, Santosh, Xin Wang, Shu Hu* (* = Corresponding author)
    ArXiv, 2024, 6.
  • Neural Radiance Fields in Medical Imaging: Challenges and Next Steps [PDF]
    Xin Wang, Shu Hu, Heng Fan, Hongtu Zhu, Xin Li
    ArXiv, 2024, 2.
  • Detecting Multimedia Generated by Large AI Models: A Survey [PDF]
    Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, Shu Hu* (* = Corresponding author)
    ArXiv, 2024, 1.
  • Improving Cross-dataset Deepfake Detection with Deep Information Decomposition [PDF]
    Shanmin Yang, Shu Hu, Bin Zhu, Ying Fu, Siwei Lyu, Xi Wu, Xin Wang
    ArXiv, 2023, 10.
  • Deep Reinforcement Learning for Image-to-Image Translation [PDF]
    Xin Wang, Ziwei Luo, Jing Hu, Chengming Feng, Shu Hu, Bin Zhu, Xi Wu, Siwei Lyu
    ArXiv, 2023, 9.
  • Fairness in Survival Analysis with Distributionally Robust Optimization [PDF]
    Shu Hu+, George H. Chen+. (+ = equal contribution)
    2023, 7.
  • Attacking Important Pixels for Anchor-free Detectors [PDF]
    Yunxu Xie+, Shu Hu+, Xin Wang, Quanyu Liao, Bin Zhu, Xi Wu, Siwei Lyu. (+ = equal contribution)
    ArXiv, 2023, 1.

Selected Papers

  • Preserving Fairness Generalization in Deepfake Detection [PDF][Code]
    Li Lin, Xinan He, Yan Ju, Xin Wang, Feng Ding, Shu Hu*. (* = Corresponding author)
    The IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2024, 6.
  • Rank-based Decomposable Losses in Machine Learning: A Survey [PDF]
    Shu Hu, Xin Wang, Siwei Lyu.
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023, 7.
  • Controlling Neural Style Transfer with Deep Reinforcement Learning [PDF]
    Chengming Feng, Jing Hu, Xin Wang, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Siwei Lyu.
    The 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023, 4.
  • Sum of Ranked Range Loss for Supervised Learning [PDF]
    Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu.
    Journal of Machine Learning Research (JMLR) , 2022, 4.
  • Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration [PDF]
    Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu and Siwei Lyu.
    AAAI Conference on Artificial Intelligence (AAAI), 2022, 2.
  • TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning [PDF]
    Shu Hu, Lipeng Ke, Xin Wang, Siwei Lyu.
    International Conference on Computer Vision (ICCV), 2021, 10.
  • Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights [PDF] [Project page] [Code] [Poster] [Award] [BBC] [WKBW-TV] [Futurism] [CNET]
    Shu Hu, Yuezun Li, Siwei Lyu.
    46th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021, 1.
  • Learning by Minimizing the Sum of Ranked Range [PDF] [Code] [Poster]
    Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu.
    Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020, 9.
  • Uncertainty Aware Semi-Supervised Learning on Graph Data [PDF]
    Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho.
    Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS)(Spotlight), 2020, 9.