Face Morphing Attack

Face morphing attacks involve digitally blending images of different individuals to create a composite that can deceive facial recognition systems (FRS). Current research focuses on improving both the generation of these attacks—using techniques like generative adversarial networks (GANs), diffusion models, and 3D point cloud manipulation—and the development of robust countermeasures, such as image purification methods and morph detection algorithms leveraging deep learning architectures (e.g., CNNs) and wavelet analysis. The ability to create increasingly realistic and undetectable morphs poses a significant security risk to applications relying on FRS, driving ongoing efforts to enhance both the accuracy and robustness of biometric authentication systems.