Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition

Sibling-Attack

The core objective of this project is to enhance attacking transferability on face recogntion systems. This approach is able to significantly improve the attacking success rate on several publicly available datasets, and break some state-of-the-art commercial platforms corresponding to face recognition models. (CVPR'2023)

Zexin Li
Zexin Li
Ph.D. Student of ECE

I am a Ph.D. student at the University of California, Riverside (UCR). I am fortunate to be advised by Dr. Cong Liu and working with Dr. Yinglun Zhu. I received a bachelor’s degree from the Southern University of Science and Technology (SUSTech) under the advice of Dr. Yuqun Zhang in July 2020. My research interests include but are not limited to the interdisciplinary fields of real-time embedded systems and on-device machine learning. I am actively looking for cooperation in the following topics - (1) deploying machine learning models on real-time embedded devices, (2) system-application co-optimization of machine learning systems, and (3) improving performance robustness in machine learning systems. Feel free to contact me if we share common research interests.