Enhancing ATM Security with Deep Learning for Movement and Tampering Detection

Patel Twinkleben Bharatbhai and Sheshang Degadwala

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 424-431

Abstract: This paper proposes an advanced deep learning-based surveillance system aimed at enhancing security in Automated Teller Machine (ATM) environments by detecting human movement and physical tampering in real time. With increasing incidents of ATM-related crimes such as skimming, cash trapping, and forced break-ins, there is a critical need for intelligent systems capable of proactively identifying threats. The study introduces a modified YOLO (You Only Look Once) architecture, termed the YOLO Attenuation Model, which incorporates architectural optimizations to improve detection accuracy while reducing computational overhead, making it ideal for real-time applications in constrained ATM hardware settings. The model was trained and validated on a custom-curated dataset comprising various ATM-related scenarios, including normal usage, suspicious loitering, forced access attempts, and equipment tampering. Extensive experiments demonstrate that the proposed system achieves an outstanding mean Average Precision (mAP) of 0.99, significantly outperforming baseline object detection models in precision, recall, and inference speed. The high detection rate ensures rapid alert generation, enabling timely responses to security breaches. Furthermore, the system’s modular design allows for easy integration with existing ATM surveillance infrastructure. This research highlights the potential of deep learning to revolutionize ATM security by providing an automated, intelligent monitoring solution capable of reducing fraud and enhancing public safety.

Keywords: ATM security; deep learning; YOLO attenuation model; tampering detection; real-time surveillance (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2512358 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2512358/IJSRST2512358 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:861

DOI: 10.32628/IJSRST2512358

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().