A NEW DEEP LEARNING FRAMEWORK FOR DETECTION FACIAL FORGERY TO ENHANCE BIOMETRIC RECOGNITION

Abstract

Face recognition systems, which are essential to contemporary security applications, are becoming more and more susceptible to spoofing attacks employing images and videos. Deep learning techniques, notably convolutional neural networks (CNNs), have emerged as viable solutions for identifying such presentation attacks. This survey examines deep learning-based face spoofing detection techniques, looking at their development, main strategies, and performance indicators. We illustrate the advantages and disadvantages of various methods by summarizing recent research, emphasizing their capacity to handle a variety of spoofing scenarios. An evaluation of present issues and potential future research avenues in this crucial area of biometric security is included at the end of the paper.