Xiangtao Meng's Homepage

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Xiangtao Meng 孟祥涛

2th-year Ph.D, ISecLab

School of Cyber Science and Technology, Shandong University

E-mail: mengxiangtao AT mail.sdu.edu.cn

[Github] [Google Scholar]

News:

  • 2025-3-11: One paper titled “Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models” got accepted in IEEE S&P 2025!

  • 2024-11-15: My master's thesis, titled “Robustness Research on Deepfake Detection Technology,” has been recognized as an outstanding master's thesis at Shandong University.

  • 2023-11-12: One paper titled “AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection” got accepted in IEEE S&P 2024!

  • 2023-10-13: One paper titled “DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models” got accepted in TOPS(ACM Transactions on Privacy and Security) 2024!

Education

Research Interests

My research centers on Trustworthy Machine Learning (expercially DeepFake), including disclosing the safety, security and privacy of the Machine Learning (such as, Text-to-Image, ChatGPT, etc.) and proposing corresponding defense measures.

Publication

  1. Yingkai Dong, Xiangtao Meng, Ning Yu, Zheng Li, Shanqing Guo. Fuzz-testing meets llm-based agents: An automated and efficient framework for jailbreaking text-to-image generation models. IEEE S&P 2025. [TOP] [CCF A] arxiv. [PDF]

  2. Xiangtao Meng, Li Wang, Shanqing Guo, Lei Ju, and Qingchuan Zhao. AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection. IEEE S&P 2024. [TOP] [CCF A] [PDF] [Code]

  3. Li Wang, Xiangtao Meng, Dan Li, Xuhong Zhang, Shouling Ji, Shanqing Guo. DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models. ACM Transactions on Privacy and Security(TOPS) 2024. [CCF B] [PDF]