SelfSuper-ID: Self-Supervised Deep Learning for Synthetic Identity Detection Under Extreme Label Sparsity - Suman Kumar Sanjeev Prasanna (International Journal of Intelligent Systems and Applications in Engineering, 2021)
Synthetic identity fraud is increasingly pervasive, yet supervised detection models often fail when labeled datasets are sparse or incomplete. This research introduces SelfSuper-ID, a self-supervised deep learning framework designed to detect synthetic identities under extreme label scarcity. The approach leverages contrastive representation learning and pseudo-label propagation to extract high-fidelity latent embeddings from multi-modal identity data, including biometric, behavioral, and transactional signals. By constructing a latent similarity graph and optimizing a cluster-aware contrastive objective, the model identifies anomalies indicative of synthetic or manipulated identities without relying on extensive labeled data. The framework also incorporates adversarial regularization to enhance robustness against emerging manipulation strategies. Empirical evaluation on large-scale, partially labeled synthetic identity datasets demonstrates that SelfSuper-ID achieves a 25–30% improvement in detection precision and recall compared to semi-supervised and unsupervised baselines, while maintaining stable performance under extreme label sparsity. These results establish self-supervised representation learning as a scalable, practical, and resilient methodology for operational identity verification in resource-constrained or rapidly evolving digital environments.