Deep Learning Approaches for Synthetic Identity Detection: Models, Challenges, and Emerging Trends

Abstract

Abstract: The rapid advancement of deep generative modeling has transformed synthetic identity fraud from a rudimentary anomaly into a sophisticated architectural challenge. This research provides a systematic analysis of the evolution of synthetic identity detection models, focusing on the transition from handcrafted statistical features to deep representation learning. The study evaluates the current state of Generative Adversarial Networks (GANs) and Diffusion Models in creating hyper-realistic identities and examines the corresponding defensive methodologies designed to identify machine-generated artifacts. The research identifies three primary technical challenges: the lack of cross-domain generalization, the increasing sophistication of multi-stage injection attacks, and the transparency-accuracy trade-off in neural detection systems. By synthesizing emerging trends in self-supervised learning and contrastive estimation, the paper offers a forward-looking perspective on the shift toward proactive, provenance-based security. This analysis provides a structured understanding of the current defensive landscape and serves as a critical guide for the development of resilient verification systems in the face of accelerating AI-driven fraud.