I obtained my Computer Science Ph.D. degree under the supervision of Prof. Tianwei Zhang in S-Lab, Nanyang Technological University, Singapore. Before that, I received my B.Eng. degree in Information Security, Mathematics from Shandong University, China.

My research focuses on critical aspects of artificial intelligence and machine learning security. Specifically, I investigate adversarial attacks and defenses, examining how malicious entities can exploit vulnerabilities in AI systems and developing robust strategies to mitigate these threats. I also explore the security of AI-generated content (AIGC), ensuring that generated outputs are safe and reliable. My work in red-teaming for models involves simulating adversarial and non-adversarial scenarios to test and improve the resilience of AI systems. Furthermore, I explore model intellectual property protection, devising methods to safeguard proprietary AI models from unauthorized access and misuse.

I joined Huawei Technologies Co., Ltd in June 2025 working on AI security.

Research Interests

• Deep Learning
• Computer Vision
• Adversarial Attack and Defense
• Backdoor Attack and Data Poison
• Security of Large Generative Models

Researches (A complete list can be found in my Google Scholar)

• Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning [pdf][code]

  Guanlin Li, Kangjie Chen, Shangwei Guo, Jie Zhang, Han Qiu, Chao Zhang, Guoyin Wang, Tianwei Zhang, Jiwei Li

  arXiv, 2025

• ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users [pdf][code]

  Guanlin Li, Kangjie Chen, Shudong Zhang, Jie Zhang, Tianwei Zhang

  NeurIPS, 2024

• Fingerprinting Image-to-Image Generative Adversarial Networks [pdf]

  Guanlin Li, Guowen Xu, Han Qiu, Shangwei Guo, Run Wang, Jiwei Li, Tianwei Zhang, Rongxing Lu

  EuroS&P, 2024

• PRIME: Protect Your Videos From Malicious Editing [pdf][code]

  Guanlin Li, Shuai Yang, Jie Zhang, Tianwei Zhang

  arXiv, 2024

• Warfare:Breaking the Watermark Protection of AI-Generated Content [pdf][code]

  Guanlin Li, Yifei Chen, Jie Zhang, Shangwei Guo, Han Qiu, Guoyin Wang, Jiwei Li, Tianwei Zhang

  arXiv, 2023

• Singular Regularization with Information Bottleneck Improves Model’s Adversarial Robustness [pdf]

  Guanlin Li, Naishan Zheng, Man Zhou, Jie Zhang, Tianwei Zhang

  arXiv, 2023

• Rethinking Adversarial Training with Neural Tangent Kernel [pdf]

  Guanlin Li, Han Qiu, Shangwei Guo, Jiwei Li, Tianwei Zhang

  arXiv, 2023

• Alleviating the Effect of Data Imbalance on Adversarial Training [pdf] [code]

  Guanlin Li, Guowen Xu, Tianwei Zhang

  arXiv, 2023

• Omnipotent Adversarial Training in the Wild [pdf] [code]

  Guanlin Li, Kangjie Chen, Yuan Xu, Han Qiu, Tianwei Zhang

  arXiv, 2023

• Extracting Robust Models with Uncertain Examples [pdf] [code]

  Guanlin Li, Guowen Xu, Shangwei Guo, Han Qiu, Jiwei Li, Tianwei Zhang

  ICLR, 2023

• Secure Decentralized Image Classification with Multiparty Homomorphic Encryption [pdf]

  Guowen Xu, Guanlin Li, Shangwei Guo, Tianwei Zhang, Hongwei Li

  IEEE Transactions on Circuits and Systems for Video Technology, 2023

• A Benchmark of Long-tailed Instance Segmentation with Noisy Labels [pdf] [code]

  Guanlin Li, Guowen Xu, Tianwei Zhang

  arXiv, 2022

• Improving Adversarial Robustness of 3D Point Cloud Classification Models [pdf] [code]

  Guanlin Li, Guowen Xu, Han Qiu, Ruan He, Jiwei Li, Tianwei Zhang

  ECCV, 2022

• Enhancing intrinsic adversarial robustness via feature pyramid decoder [pdf] [code]

  Guanlin Li, Shuya Ding, Jun Luo, Chang Liu

  CVPR, 2020

• Scnet: A neural network for automated side-channel attack [pdf] [code]

  Guanlin Li, Chang Liu, Han Yu, Yanhong Fan, Libang Zhang, Zongyue Wang, Meiqin Wang

  arXiv, 2020

Professional Services

Conference Reviewer for ICML, NeurIPS, ICLR, ECCV, ICCV and CVPR