Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?

no code implementations • 30 Nov 2023 • Zhengyue Zhao, Jinhao Duan, Kaidi Xu, Chenan Wang, Rui Zhangp Zidong Dup Qi Guo, Xing Hu

Although these studies have demonstrated the ability to protect images, it is essential to consider that these methods may not be entirely applicable in real-world scenarios.

Pushing the Limits of Machine Design: Automated CPU Design with AI

1 code implementation • 21 Jun 2023 • Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen

By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours.

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

no code implementations • 2 Jun 2023 • Zhengyue Zhao, Jinhao Duan, Xing Hu, Kaidi Xu, Chenan Wang, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen

This imperceptible protective noise makes the data almost unlearnable for diffusion models, i. e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data.

Denoising Image Generation