With the increasing deployment of face recognition systems for security and authentication, the need for robust face anti-spoofing techniques has become paramount to prevent spoofing attacks using photos, videos, or 3D masks. Traditional methods have relied on manual feature extraction to distinguish between real and spoofed faces. However, recent advancements have shifted towards Deep Learning (DL) approaches, which automatically learn complex feature representations from raw data. Deep learning models, particularly CNNs, have demonstrated significant potential in face anti-spoofing by leveraging spatial and temporal information from images and videos. The primary goal of this research is to classify face inputs into real or spoof categories using deep learning architectures. Large datasets comprising both genuine and spoofed face samples are utilized, with models trained to recognize subtle differences in texture, depth, and motion. Pre-trained deep neural networks, including CNNs and Recurrent Neural Networks (RNNs), have been fine-tuned for this task. Additionally, data augmentation and regularization techniques are employed to improve model generalization. The proposed approach is validated using standard face anti-spoofing datasets, and results demonstrate that deep learning methods outperform traditional techniques, achieving an accuracy rate of up to 99.93%.