🎈 About Me
I am currently a third-year Ph.D. student at AI Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou) supervised by Prof. Li Liu and Prof. Hui Xiong. Previously, I received my master degree from City University of Hong Kong, advised by Prof. Xiangyu Zhao.
Research Interests
- AI Security / Safety
- Backdoor Learning
- Large Audio Language Model
- Recommendation
📝 Selected Papers
(* indicates equal contribution, # indicates corresponding author)
Publications

BackdoorDM: A Comprehensive Benchmark for Backdoor Learning on Diffusion Model
Weilin Lin*, Nanjun Zhou*, Yanyun Wang, Jianze Li, Hui Xiong, Li Liu#
- The first comprehensive benchmark for backdoor learning on diffusion models.
- Propose a unified attack formulation and a systematic target taxonomy.
- Support 9 diffusion backdoor attacks, 5 defense methods, and 3 visualization tools.
The Thirty-Ninth Annual Conference on Neural Information Processing Systems Datasets & Benchmarks Track (NeurIPS D&B), San Diego, California, USA, 2025

Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation Oral Presentation (4.6%)
Weilin Lin, Li Liu#, Jianze Li, Hui Xiong
- One of the few data-free defense strategies against backdoor attacks.
- First adaptation of OT and model fusion on backdoor defense.
The Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), Philadelphia, Pennsylvania, USA, 2025

Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness
Weilin Lin, Li Liu#, Shaokui Wei, Jianze Li, Hui Xiong
- New insights on unlearning weight change and backdoor activeness.
- Propose an effective defense strategy using reinitialization and fine-tuning.
Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
ICASSP 2025
Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech Recognition, Nanjun Zhou*, Weilin Lin*, Li Liu#.WWW 2023
Autodenoise: Automatic data instance denoising for recommendations, Weilin Lin, Xiangyu Zhao#, Yejing Wang, Yuanshao Zhu, Wanyu Wang.KDD 2022
AdaFS: Adaptive feature selection in deep recommender system, Weilin Lin, Xiangyu Zhao#, Yejing Wang, Tong Xu, Xian Wu.
Preprints
Arxiv 2024
Segment anything for videos: A systematic survey, Chunhui Zhang, Yawen Cui, Weilin Lin, Guanjie Huang, Yan Rong, Li Liu#, Shiguang Shan.Arxiv 2023
A comprehensive survey on segment anything model for vision and beyond, Chunhui Zhang, Li Liu#, Yawen Cui, Guanjie Huang, Weilin Lin, Yiqian Yang, Yuehong Hu.
📖 Educations
- Ph.D., Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), September 2023 - Present
- Advisors: Prof. Li Liu and Prof. Hui Xiong
- M.S.c, Multimedia Information Technology, City University of Hong Kong, August 2021 - October 2022
- Advisor: Prof. Xiangyu Zhao
- B.S., Electronic Information Science and Technology, South China Normal University, September 2017 - July 2021
🧭 Experience
- 2023.03 - 2023.09, Research Assistant, The Hong Kong University of Science and Technology (Guangzhou). Advisor: Prof. Li Liu.
- 2022.07 - 2023.03, Research Assistant, USAIL Group, HKUST Fok Ying Tung Research Institute / The Hong Kong University of Science and Technology (Guangzhou). Advisor: Prof. Hao Liu.
- 2021.09 - 2022.10, Research Assistant, Applied Machine Learning Lab (AML Lab), City University of Hong Kong. Advisor: Prof. Xiangyu Zhao.
🎖 Honors and Awards
- Best Student Paper Award Finalist in ICSR 2024.
- Full Postgraduate Scholarship, HKUST(GZ).
🍀 Services
Reviewer/External Reviewer
- The International Conference on Learning Representations (ICLR), 2026
- The International Conference on Machine Learning (ICML), 2025
- Annual Conference on Neural Information Processing Systems (NeurIPS), 2024, 2025
- Annual AAAI Conference on Artificial Intelligence (AAAI), 2024, 2025, 2026
- Conference on Research and Development in Information Retrieval (SIGIR), 2024