no code implementations • 17 Mar 2024 • Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, Haibing Guan
In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication.
1 code implementation • 7 Oct 2023 • BoYang Zheng, Chumeng Liang, Xiaoyu Wu, Yan Liu
We show that these attacks add an extra error to the score function of adversarial examples predicted by LDM.
1 code implementation • 2 Oct 2023 • Haotian Xue, Chumeng Liang, Xiaoyu Wu, Yongxin Chen
In this work, we present novel findings on attacking latent diffusion models (LDM) and propose new plug-and-play strategies for more effective protection.
1 code implementation • 19 Jun 2023 • Zhanyu Liu, Chumeng Liang, Guanjie Zheng, Hua Wei
Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.
1 code implementation • 22 May 2023 • Chumeng Liang, Xiaoyu Wu
Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright.
1 code implementation • 9 Feb 2023 • Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
1 code implementation • 3 Oct 2022 • Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Kan Wu, Yuhao Du, Fuliang Li, Zhenhui Li
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.