Abstract Generative Adversarial Networks (GANs) are increasingly used in biometric systems. However, existing signature studies predominantly focus on strengthening discriminators or producing data for augmentation, leaving the quality and spoofing capability of generated forgeries insufficiently examined. To address this research gap, we propose Block-Induced Signature GAN (BISGAN )—a generator- focused architecture integrating inception-style blocks and attention mechanisms to preserve influen
Block induced signature generative adversarial network (BISGAN): signature spoofing using GANs
Haadia Amjad·Muhammad Imran Malik·Kilian Göller·Muhammad Naseer Bajwa·Ronald Tetzlaff·Carsten Knoll·Steffen Seitz