Deep Learning Models for Image and Voice-Based Authentication Systems

Abstract

Authentication systems are critical for ensuring the security and privacy of users in digital environments. Traditional authentication methods, such as passwords and PINs, are often vulnerable to security breaches. Recent advancements in deep learning have enabled the development of more secure and efficient image and voice-based authentication systems. This article explores the role of deep learning models, such as convolutional neural networks (CNNs) for image authentication and recurrent neural networks (RNNs) for voice authentication, in enhancing the accuracy and security of biometric authentication systems. We discuss the benefits, challenges, and future directions for these systems in real-world applications.