Cloud-Based Intelligent Surveillance for Digital Forensics: AI-Enhanced Criminal Investigations

Modern smart surveillance systems have become a core element of digital forensics workflows, offering real-time detection of weapons, fire, smoke, blood, cars, individuals, and other related objects. These systems improve evidence collection, accelerate threat identification, and enhance investigative efficiency. However, most traditional surveillance architectures operate without sufficient digital forensic awareness, evidence integrity mechanisms, standardized evidence management, or reliable methods for detecting tampering. These systems rarely support chain of custody documentation and often lose reliability under adverse conditions. To address these shortcomings, a unified system that aggregates all suspicious objects and movements is needed, geared towards forensics. Therefore, we developed VigilEye, a cloud-oriented forensic surveillance framework that combines public surveillance datasets with simulated crime scene scenarios. It applies digital forensic preprocessing, such as CLAHE, to improve clarity, and SHA-256 hashing and metadata to ensure evidence integrity. Previous studies have also highlighted privacy challenges; we addressed this by sending blurred images for alerts, while the original, unblurred images are designed to be securely stored in an encrypted evidence environment accessible only to authorized personnel. This study presents the design, implementation, and digital forensic evaluation of VigilEye. The model achieved promising experimental performance (mAP50 ≈ 0.72) with an average detection speed of 33.55 ms per image, indicating that further optimization is required to enhance both speed and accuracy. The system sends alerts via the Telegram platform if it detects suspicious behavior or wanted individuals. VigilEye demonstrates the feasibility of a forensic-aware surveillance workflow with real-time alerting and integrity-preserving evidence handling in an experimental setting. Our future work includes expanding digital forensic datasets, improving spatial-temporal event detection, automating cloud-based chain of custody management, and enhancing interoperability with tools such as Autopsy and FTK Imager, alongside strengthening privacy safeguards and connected camera digital forensic tracking.

@article{Aljuhani2026,
  title     = {Cloud-Based Intelligent Surveillance for Digital Forensics: AI-Enhanced Criminal Investigations},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Aseel Abdullah Aljuhani and Fatima Hamed Aljuhani},
  doi       = {10.14569/IJACSA.2026.0170513},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170513}
}