Model Orthogonalization - Class Distance Hardening in Neural Networks for Better Security

Model Orthogonalization - Class Distance Hardening in Neural Networks for Better Security

Explore a 19-minute IEEE conference talk on enhancing neural network security through model orthogonalization and class distance hardening. Delve into the research conducted by a team from Purdue University, including Guanhong Tao, Yingqi Liu, Guangyu Shen, Qiuling Xu, Shengwei An, Zhuo Zhang, and Xiangyu Zhang. Learn about innovative techniques to improve the robustness and reliability of neural networks in the face of security challenges, and gain insights into the potential applications of these methods in various fields of artificial intelligence and machine learning.