I am currently a graduate student in Computer Science at University of Southern California. My goal is to make state-of-the-art generative models truly beneficial to the society. Currently, I focus on the trustworthy and copyright concerns of diffusion models. Meanwhile, I am building scalable methods for augmenting diffusion models and LLM in real-world applications.
Mist-v2 has released! Compared to Mist, Mist-v2 enjoys far more powerful protection performance against LoRA. We update its detailed features in the Homepage. We are also going to announce it in X. Looking forward to it!
Aug 21, 2023
I am attending the University of Southern California and become a first-year Ph.D. student!
May 10, 2023
Our project on adding adversarial watermarks against unauthorized artwork copying with Stable Diffusion, Mist, is now open-sourced on GitHub. Try to protect your artworks by adding tiny watermarks on them!
@article{wu2024cgi,title={CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion},author={Wu, Xiaoyu and Hua, Yang and Liang, Chumeng and Zhang, Jiaru and Wang, Hao and Song, Tao and Guan, Haibing},journal={arXiv preprint arXiv:2403.11162},year={2024}}
ICLR
Toward effective protection against diffusion based mimicry through score distillation
Haotian Xue, Chumeng Liang, Xiaoyu Wu, and 1 more author
@inproceedings{xue2023toward,title={Toward effective protection against diffusion based mimicry through score distillation},author={Xue, Haotian and Liang, Chumeng and Wu, Xiaoyu and Chen, Yongxin},booktitle={arXiv preprint arXiv:2311.12832},year={2024}}
2023
ICMLOral
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Chumeng Liang*, Xiaoyu Wu*, Yang Hua, and 6 more authors
In Proceedings of the 40th International Conference on Machine Learning, 2023
@inproceedings{pmlr-v202-liang23g,tag={Oral},title={Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples},author={Liang*, Chumeng and Wu*, Xiaoyu and Hua, Yang and Zhang, Jiaru and Xue, Yiming and Song, Tao and Xue, Zhengui and Ma, Ruhui and Guan, Haibing},booktitle={Proceedings of the 40th International Conference on Machine Learning},pages={20763--20786},year={2023}}
KDD
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
Chumeng Liang, Zherui Huang, Yicheng Liu, and 7 more authors
In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
@inproceedings{liang2023cblab,title={CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation},author={Liang, Chumeng and Huang, Zherui and Liu, Yicheng and Liu, Zhanyu and Zheng, Guanjie and Shi, Hanyuan and Wu, Kan and Du, Yuhao and Li, Fuliang and Li, Zhenhui Jessie},booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},pages={4449--4460},year={2023}}
ECML-PKDD
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Zhanyu Liu, Chumeng Liang, Guanjie Zheng, and 1 more author
@article{liu2023fdti,title={FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph},author={Liu, Zhanyu and Liang, Chumeng and Zheng, Guanjie and Wei, Hua},journal={arXiv preprint arXiv:2306.10945},year={2023}}
