Artificial Fingerprint

Artificial fingerprint generation focuses on creating realistic synthetic fingerprints for research and application purposes, addressing limitations in real data availability and privacy concerns. Current research employs various generative models, including Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs), to produce high-quality, diverse, and even revocable fingerprints. This work is significant because it expands the availability of training data for biometric systems, improves the security and privacy of biometric authentication, and enables the development of methods to detect synthetically generated images.