Anti-Spoofing: Liveness Detection System

Abstract: Face recognition systems became more susceptible to presentation attacks by digital screens, printed images, and 3D masks [3]. This paper introduces a full-fledged anti-spoofing solution based on the YOLO (You Only Look Once) frameworktoidentifyandthwart suchattemptsatspoofinginreal-time[14]. Oursystemintegrates effective object detection features with custom liveness evaluation features to form an effective security layer for biometric authentication systems. Experimental results show high accuracy in distinguishing between real users and spoofing attempts with real-time performance appropriate for practical use [4]. The study points out the efficiency of feature extraction from biometric informationusingCNNs [16] and the capacity ofTransformers to model global dependencies for improvedspoof detection [11]. By combining these approaches, the study seeks to enhance the accuracy and reliability of liveness detection, mitigating vulnerabilities in biometric authentication systems [9].