Deep Learning-Based Face Morphing Detection for Identity Verification

Face morphing, the process of combining two or more facial images to create a new face, particularly in identity verification. Such morphing attacks can be used to bypass security systems that rely on facial recognition, undermining the integrity and reliability of these systems. This paper explores the use of deep learning techniques for detecting face morphing attacks, focusing on Convolutional Neural Networks (CNNs) as a primary method for feature extraction and morphing detection. We propose a novel framework that combines data preprocessing, feature learning, and classification to identify subtle artifacts introduced during the morphing process. A large dataset of both morphed and real face images is utilized to train the model, allowing it to automatically learn distinguishing patterns that are typically undetectable by traditional methods. The proposed system is evaluated on various performance metrics, including accuracy, precision, and recall, to assess its ability to generalize across different morphing techniques and face variations. Our results show that deep learning-based models, particularly CNNs, can significantly enhance the detection of face morphing attacks, offering a robust solution for improving the security of identity verification systems. This work contributes to the growing need for reliable biometric security solutions in an era where face morphing techniques continue to evolve and pose threats to identity verification systems.