Published in arXiv preprint, 2023
Mist improves the transferability and robustness of adversarial watermarks designed to protect artwork from unauthorized diffusion-model imitation.
Recommended citation: Liang, C.* and Wu, X.* (2023). "Mist: Towards Improved Adversarial Examples for Diffusion Models." arXiv:2305.12683.
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Published in ICML 2023 (Oral), 2023
AdvDM uses adversarial examples as protective watermarks that prevent diffusion models from learning an artist’s style from unauthorized images.
Recommended citation: 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 presentation.
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Published in CVPR 2024, 2024
CGI-DM visualizes conceptual differences between pretrained and fine-tuned diffusion models to provide evidence for copyright authentication.
Recommended citation: Wu, X., Hua, Y., Liang, C., et al. (2024). "CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion." CVPR 2024.
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Published in ICLR 2025 (Spotlight), 2025
Carefully selected targeted attacks provide stronger protection against unauthorized diffusion customization than untargeted adversarial watermarks.
Recommended citation: Zheng, B., Liang, C., and Wu, X. (2025). "Targeted Attack Improves Protection against Unauthorized Diffusion Customization." ICLR 2025. Spotlight presentation.
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Published in ICML 2025, 2025
FineXtract uses the distribution shift between pretrained and personalized diffusion models to recover about 20% of fine-tuning data from real-world checkpoints.
Recommended citation: Wu, X., Zhang, J., and Wu, S. (2025). "Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models." ICML 2025.
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Published in TPDP 2025, 2025
A lightweight learned membership inference attack reveals privacy leakage in diffusion-based tabular synthesis and won all four tracks of the MIDST Challenge.
Recommended citation: Wu, X., Pang, Y., Liu, T., and Wu, Z. S. (2025). "Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis." TPDP 2025.
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Published in NeurIPS 2025, 2025
Exact unlearning can still leak removed examples when pre- and post-unlearning models are exposed; our guided attack doubles extraction success in some settings.
Recommended citation: Wu, X., Pang, Y., Liu, T., and Wu, Z. S. (2025). "Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM." NeurIPS 2025.
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Published in KDD 2026, 2026
We identify a corruption stage during few-shot diffusion fine-tuning and use Bayesian neural networks to improve fidelity, quality, and diversity without extra inference cost.
Recommended citation: Wu, X.*, Zhang, J.*, Hua, Y., et al. (2026). "Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks." KDD 2026.
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Published in arXiv preprint, 2026
Dual-Stage Registers reduce harmful outlier tokens in both vision encoders and diffusion transformers, improving generation quality across ImageNet and text-to-image settings.
Recommended citation: Wu, X.*, Wang, Y.*, Fu, T.-J., et al. (2026). "Taming Outlier Tokens in Diffusion Transformers." arXiv:2605.05206.
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