Gradient-Penalty GAN Framework for High-Fidelity Fingerprint Synthesis.

Abstract

The rapid advancements in generative adversarial networks (GANs) have significantly impacted digital content synthesis, presenting both opportunities and challenges in multimedia forensics and cybersecurity. We present an Enhanced Adaptive DCGAN (EADC-GAN) for generating high-fidelity synthetic fingerprints, addressing core challenges in training stability and sample diversity. By combining Wasserstein loss with gradient penalty (WGAN-GP), instance normalization in the discriminator, and tailored architectural refinements, our model achieves strong image realism at reduced training cost. Compared to prior DCGAN-based methods, EADC-GAN synthesizes more diverse, artifact-free samples in fewer epochs, making it suitable for scalable biometric data generation. This has key implications for secure authentication, privacy-preserving biometric datasets, and adversarial robustness in cybersecurity contexts.