Morph Detection

Morph detection focuses on identifying manipulated images, particularly face morphs created by combining features from multiple individuals, which pose a significant threat to security systems relying on facial recognition. Current research emphasizes developing robust detection models, employing deep learning architectures like convolutional neural networks and transformers, often enhanced by techniques such as adversarial training and wavelet decomposition to improve generalization and resilience against sophisticated morphing attacks. These advancements are crucial for enhancing the security and reliability of biometric authentication systems and other applications vulnerable to image manipulation.