Education

• M.S. in Shanghai Jiao Tong University, 2025 (expected)
• B.S. in Shanghai Jiao Tong University, 2022

Research Interests

• Trustworthy AI: Mainly on copyright protection and authentication w./against AI personalization.
• Privacy and Security: Adversarial attack and defense, data extraction, membship inference attack
• Diffusion Models

Research Experience

[Under Review] Data Extraction on Personalized Generative Models
Instructor: Steven Wu (CMU)
May. 2024 — Present

• Revealed the data leakage threat for large personalized generative models by performing successful exact data extraction, providing strong evidence of potential infringements.
• Utilized guidance between pretrained and fine-tuned models to generate within high-density regions of the fine-tuned data probability distribution, enabling precise data extraction.

[ICML 2023 (Oral) & CVPR 2024] Copyright Authentication and Imitation Prevention for Diffusion Models
Oct. 2022 — May. 2024
Instructor: Yang Hua (QUB), Hao Wang (LSU), Tao Song (SJTU)

• Crafted a visualizable copyright authentication mechanism by modeling and analyzing the conceptual difference between fine-tuned and pretrained diffusion models theoretically.
• Established a framework to craft adversarial watermarks against unauthorized diffusion model-based artwork mimicry by modeling adversarial attacks on diffusion models theoretically.

Selected Publications

Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data

Wu X, Zhang J, Wu S. (2024). "Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data." arXiv preprint arXiv:2410.03039.

Exploring Diffusion Models’ Corruption Stage in Few-Shot Fine-Tuning and Mitigating with Bayesian Neural Networks

Wu X*, Zhang J*, Hua Y, et al. (2024). "Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-Tuning and Mitigating with Bayesian Neural Networks." arXiv preprint arXiv:2405.19931. (Co-First Author)

CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion

Wu X, Hua Y, Liang C, et al. (2024). "CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion." CVPR 2024.

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Liang C*, Wu X*, Hua Y, et al. (2023). "Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples." ICML 2023 (Oral). (Co-First Author)

Service

• Reviewers for NeurIPS 2024, ICLR 2025.