Morphing Attack

Face morphing attacks involve creating composite images blending multiple identities to deceive face recognition systems, primarily aiming to bypass security measures in applications like border control and identity verification. Current research focuses on generating increasingly realistic morphs using techniques like generative adversarial networks (GANs) and diffusion models, as well as developing robust detection methods employing deep learning architectures, including convolutional neural networks (CNNs) and specialized loss functions, often incorporating multispectral imaging or quality assessment metrics. The ability to generate and detect these attacks is crucial for maintaining the security and reliability of biometric authentication systems, with ongoing research striving to improve both the realism of attacks and the accuracy of detection algorithms.