High-Performance Face Identification using Hyperspectral Imaging to Counteract Deep Fake Biometrics

This research addresses the security challenges posed by Generative Adversarial Networks (GANs) in biometric authentication, focusing particularly on the use of Hyperspectral Imaging (HSI) to counteract the threat of deep fake biometrics created by GANs. We sought solutions to the issues identified in a previous study, such as the inability to handle images of individuals not previously trained and the high time consumption required for identification. In this paper, we first reduced the dimensionality of hyperspectral face data used for training, and then conducted a binary classification of “target subject (class)” and “nontarget subjects (classes).” A discriminator, based on the Least Squares Generative Adversarial Network (LSGAN) model, was developed using HSI for each class for binary classification. The proposed method significantly reduced identification time and achieved very high accuracy in identification.