AI-Driven Anti-Spoofing for Robust Real-Time Face Recognition The security of facial recognition systems has become a critical challenge, especially in scenarios where real-time operation is required. This thesis introduces an innovative approach that integrates edge AI to prevent and counter spoofing attempts. A thorough review of the state-of-the-art reveals that many existing anti-spoofing solutions rely on additional hardware, such as costly and complex Time-of-Flight (TOF) sensors, or on traditional methods that require specific environmental conditions to function properly. These limitations hinder their scalability and practical deployment in real-world applications. To overcome these challenges, this thesis introduces an innovative edge AI approach that integrates deep learning techniques optimized for devices with limited computational resources.