A Survey on Crime Detection and Prevention Techniques using Modern Computational Approaches

Abstract

In the digital age, crime management systems have evolved significantly, integrating technologies such as cloud computing, secure databases, facial recognition, and real-time communication.This paper presents a comprehensive survey of research efforts focused on digital crime reporting, predictive analysis, and intelligent surveillance.Key contributions from recent literature include encrypted data storage for sensitive criminal records, use of machine learning for pattern detection, and secure mobile-based citizen reporting mechanisms.Techniques like facial detection using MTCNN, end-to-end encryption via Signal Protocol, and hybrid CNN-SVM models for incident classification have been explored.We also examine cloud-based storage architectures emphasizing data integrity and access control.The challenges of metadata leakage, privacy trade-offs, and model scalability are discussed.This survey aims to provide a foundational understanding of current methodologies and their effectiveness, while identifying potential areas for improvement in creating scalable, secure, and citizen-friendly crime management infrastructures.

Introduction

Rapid urbanization and growing socio-economic complexity have led to increased crime rates worldwide, exposing the limitations of traditional crime reporting and management systems that lack real-time responsiveness, advanced analytics, and secure data handling. To address these challenges, modern crime management increasingly integrates advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, Blockchain, and End-to-End Encryption (E2EE).

Crime data’s sensitive nature demands robust security measures including encryption (AES, Signal Protocol), hashed databases, and role-based access controls to protect confidentiality and integrity. AI and ML techniques (e.g., CNNs, SVMs, MTCNN for facial detection) enhance crime pattern analysis, hotspot prediction, and suspect identification, improving law enforcement effectiveness. Mobile-based citizen reporting platforms with features like geotagging and anonymity have boosted public engagement, while cloud platforms enable scalable, collaborative, and resilient data management.

Despite these advancements, challenges remain in data processing latency, metadata exposure, cross-jurisdiction interoperability, and public awareness. The paper reviews relevant literature on AI-driven face recognition, hybrid CNN-SVM classifiers, and various E2EE protocols, noting their strengths and limitations such as computational demands, scalability issues, and vulnerabilities.

A proposed integrated system combines four key modules: an End-to-End Encryption Engine (using Signal Protocol for secure communication), a Face Recognition Module (based on MTCNN for accurate detection under varied conditions), a Hybrid Classification Unit (combining CNN and SVM for robust classification), and a Multi-Modal Integration Layer that securely merges outputs for real-time, scalable performance. This architecture delivers high accuracy, strong security, scalability, and real-time capabilities, demonstrating a promising framework for next-generation crime management solutions.

Conclusion

Crime detection and prevention systems are rapidly evolving with the integration of AI, ML, cloud computing, and end-to-end encryption. Modern approaches leverage facial recognition, hybrid deep learning models, and secure data management to enhance accuracy and reliability. Literature highlights significant advancements in encrypted storage, predictive policing, and citizen-centric reporting platforms. Despite progress, challenges remain in scalability, interoperability, and privacy preservation. Overall, these computational approaches present promising directions for building intelligent, secure, and efficient crime management frameworks.

References

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Copyright

Copyright © 2025 Anuradha Badage, Amber Mishra, Aniket Vishnu, Aryan Raj Singh, B. V. Anish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.