Face Morphing Attack Detection

Face morphing attacks, which involve blending multiple facial images to create a convincing forgery, pose a significant threat to face recognition systems used in security applications. Current research focuses on developing robust detection methods, employing techniques like deep convolutional neural networks, wavelet scattering networks, and autoencoders, often combined with ensemble or fusion strategies to improve accuracy and interpretability. These efforts are crucial for enhancing the security and reliability of biometric authentication systems, particularly in sensitive areas like border control, and address concerns about algorithmic bias and the need for explainable AI in this critical domain.