Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security

2024-02-02

AI Summary

To address critical challenges in urban regional analysis—including strong noise interference, severe data sparsity, and fragile privacy security—this paper proposes an adversarial self-supervised contrastive learning framework tailored for dynamic, noisy environments. The method innovatively integrates perturbation augmentation, a deception generator, and a bias replication generator to jointly enhance embedding robustness and defend against adversarial attacks. It further incorporates adversarial training, multi-objective joint optimization (combining supervised and self-supervised objectives), and generative data augmentation. Evaluated on three core urban tasks—crime prediction, check-in prediction, and land-use classification—the framework achieves up to 10.8% accuracy improvement over state-of-the-art methods. Moreover, it demonstrates superior robustness under diverse adversarial attacks. This work establishes a novel paradigm for urban spatiotemporal modeling under high-noise, low-data-quality, and stringent privacy constraints.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Urban region profiling plays a crucial role in forecasting and decision-making in the context of dynamic and noisy urban environments. Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities. This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address these challenges. By combining adversarial contrastive learning with both supervised and self-supervised objectives, EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification. To enhance model resilience against adversarial attacks and noisy data, we incorporate several key components, including perturbation augmentation, trickster generator, and deviation copy generator. These innovations effectively improve the robustness of the embeddings, making EUPAS capable of handling the complexities and noise inherent in urban data. Experimental results show that EUPAS significantly outperforms state-of-the-art methods across multiple tasks, achieving improvements in prediction accuracy of up to 10.8%. Notably, our model excels in adversarial attack tests, demonstrating its resilience in real-world, security-sensitive applications. This work makes a substantial contribution to the field of urban analytics by offering a more robust and secure approach to forecasting and profiling urban regions. It addresses key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.

Problem

• Urban Area Analysis
• Data Incompleteness
• Security Issues

Innovation

• EUPAS
• Adversarial Self-supervised Learning
• Urban Informatics