Adversarial Self-Supervised Learning for Secure and Robust Urban Region Profiling

Urban region profiling is essential for forecasting and decision-making in dynamic and noisy urban environments. However, existing approaches struggle with adversarial attacks, data incompleteness, and security vulnerabilities, which undermine predictive accuracy and reliability. This paper introduces Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), a robust framework that integrates adversarial contrastive learning with self-supervised and supervised objectives. To fortify resilience against adversarial attacks and noisy data, we introduce perturbation augmentation, a trickster generator, and a deviation copy generator, which collectively enhance the robustness of learned embeddings. EUPAS significantly outperforms state-of-the-art models in forecasting tasks, including crime prediction, check-in prediction, and land use classification, achieving up to 12.2% improvement in forecasting performance. Additionally, our model demonstrates superior resilience against transfer-based black-box and white-box attacks compared to baseline models. By addressing key security challenges in data-driven urban modeling, EUPAS provides a scalable and adversarially robust solution for smart city applications.