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 influential biometric features during forgery generation. We further introduce a train-shift learning strategy, grounded in adversarial robustness theory and the Resource-Based View (RBV), which enhances the generator’s ability to mimic authentic signature traits. Experiments on benchmark datasets demonstrate that BISGAN achieves 88%–100% spoofing success, exceeding prior GAN-based approaches by at least 12%. To support objective assessment, we develop a Generated Quality Metric (GQM) that evaluates forgery realism using latent feature distribution distances. The results confirm the importance of generator-centric adversarial modeling for advancing the robustness and security evaluation of signature verification systems.