  This study investigates the vulnerabilities of data-driven offline signature
verification (DASV) systems to generative attacks and proposes robust
countermeasures. Specifically, we explore the efficacy of Variational
Autoencoders (VAEs) and Conditional Generative Adversarial Networks (CGANs) in
creating deceptive signatures that challenge DASV systems. Using the Structural
Similarity Index (SSIM) to evaluate the quality of forged signatures, we assess
their impact on DASV systems built with Xception, ResNet152V2, and DenseNet201
architectures. Initial results showed False Accept Rates (FARs) ranging from 0%
to 5.47% across all models and datasets. However, exposure to synthetic
signatures significantly increased FARs, with rates ranging from 19.12% to
61.64%. The proposed countermeasure, i.e., retraining the models with real +
synthetic datasets, was very effective, reducing FARs between 0% and 0.99%.
These findings emphasize the necessity of investigating vulnerabilities in
security systems like DASV and reinforce the role of generative methods in
enhancing the security of data-driven systems.
