Generative Adversarial Network (GAN) Development for Counter-Fraud Training

The digital revolution has significantly improved how personal information
is processed and secured, but it also faces challenges like escalating identity
document fraud. Fraudsters are using advanced technologies like artificial
intelligence (AI) to create fake identity documents, posing threats from financial
fraud to terrorism. This project aims to use Generative Adversarial Networks
(GANs), a sophisticated AI tool, to create synthetic yet realistic identity documents. These documents will help train machine learning (ML) models without ethical and privacy issues of using real data. The project's goals include developing GAN based methods to generate diverse synthetic identity documents, enhancing ML models' ability to detect fraud, creating a dataset of forged documents for research and training, and evaluating the effectiveness of GAN-generated datasets in ML training. The project also aims to provide policy and technical guidance for integrating these systems into existing security infrastructures.