Abstract
Abstract: Face morphing attacks are a growing concern in the digital world, with the potential to compromise personal privacy and security. In this project, we investigate the application of deep learning methods for both generating and detecting face morphing attacks. For the generation of morphed, we use a combination of convolutional neural networks and auto encoders to learn the underlying facial features and generate realistic-looking images. On the detection side, we develop a framework based on facial feature consistency analysis to differentiate distorted images from real ones. The proposed framework achieves high accuracy in detecting face morphing attacks, even in cases where the morphed images are visually similar to the genuine ones. This project highlights the potential of deep-learning based approaches for addressing the problem of face morphing attacks and provides insights for further research and development in this area
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