| Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples C Liang, X Wu, Y Hua, J Zhang, Y Xue, T Song, Z Xue, R Ma, H Guan International Conference on Machine Learning, 20763-20786, 2023 | 26 | 2023 |
| CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation C Liang, Z Huang, Y Liu, Z Liu, G Zheng, H Shi, K Wu, Y Du, F Li, ZJ Li Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 9* | 2023 |
| Mist: Towards improved adversarial examples for diffusion models C Liang, X Wu arXiv preprint arXiv:2305.12683, 2023 | 8 | 2023 |
| Understanding and Improving Adversarial Attacks on Latent Diffusion Model B Zheng, C Liang, X Wu, Y Liu arXiv preprint arXiv:2310.04687, 2023 | 5 | 2023 |
| FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph Z Liu, C Liang, G Zheng, H Wei Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 3 | 2023 |
| Toward effective protection against diffusion-based mimicry through score distillation H Xue, C Liang, X Wu, Y Chen The Twelfth International Conference on Learning Representations, 2023 | 2 | 2023 |
| CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion X Wu, Y Hua, C Liang, J Zhang, H Wang, T Song, H Guan arXiv preprint arXiv:2403.11162, 2024 | | 2024 |
| MOTSC: Model-based Offline Traffic Signal Control Y Liu, C Liang, Z Huang, G Zheng | | 2023 |
Guanjie ZhengShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
