Adv-ID: Adversarial Frontiers in Analyzing Vulnerabilities and Defensive Countermeasures in Synthetic Identity Detection

Abstract

Synthetic identity fraud has emerged as a critical challenge in digital security, with sophisticated adversaries exploiting the limitations of automated detection systems. This research introduces Adv-ID, a systematic framework for analyzing adversarial vulnerabilities in synthetic identity detection models. The study evaluates deep learning architectures, including convolutional, recurrent, and graph-based networks against targeted attacks designed to evade detection via adversarial perturbations and latent-space manipulations. A key technical innovation of this work is the introduction of a Hybrid Adversarial Training (HAT) regimen that incorporates perturbed hard examples into the training loop, forcing the model to learn more robust decision boundaries across biometric and behavioral datasets. The research identifies critical weaknesses in current architectures regarding feature representation and cross-modal consistency. Empirical evaluation demonstrates that while vanilla models remain highly susceptible to targeted evasion, the proposed Adv-ID defensive framework reduces the attack success rate by over 60% while maintaining detection precision. These results highlight the necessity of proactive, adversary-aware design strategies and provide a scalable methodology for securing digital identity infrastructures against the next generation of machine-generated adversarial threats.