Real-Time AI-Driven Exam Cheating Detection Using YOLO and Pose Estimation: A Multi-Modal Deep Learning Approach

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@article{1712742,
      author = {Abdulrahman Abdulkarim, Muhammed Kuliya, Aisha Bappa Adam},
      title = {Real-Time AI-Driven Exam Cheating Detection Using YOLO and Pose Estimation: A Multi-Modal Deep Learning Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {847-856},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712742.pdf},
      abstract = {The preservation of academic integrity in examination environments is a cornerstone of credible certification. Traditional invigilation methods, reliant on human monitors, are inherently limited by factors such as fatigue, subjective bias, and an inability to monitor large-scale settings simultaneously. This paper proposes a novel, end-to-end, real-time AI-driven system for the automated detection of exam cheating. Our solution employs a multi-modal framework that combines state-of-the-art object detection with sophisticated human pose estimation. We implement and fine-tune a YOLOv8 (You Only Look Once) model for the real-time identification of prohibited objects, including mobile phones, micro-earpieces, and written notes. Concurrently, an OpenPose-based pose estimation pipeline analyzes candidates' body language to flag suspicious postures, such as excessive head rotation for gaze estimation, abnormal body orientation, and furtive hand movements. A central decision module fuses these dual streams of visual evidence using a rule-based heuristic to generate low-latency, high-confidence alerts for human invigilators. To validate our system, we curated a comprehensive custom dataset comprising 50 hours of annotated exam footage. Experimental results reveal that our fused model achieves a precision of 0.92, a recall of 0.88, and F1-score of 0.90, significantly outperforming unimodal baselines (YOLO-only and Pose-only). The system operates at an average latency of 35 ms per frame, fulfilling the stringent requirements for real-time video surveillance. This work establishes a robust, scalable, and efficient paradigm for proactive exam integrity enforcement, mitigating the limitations of human-based monitoring.},
      keywords = {AI-driven, Object Detection, YOLOv8, Human Pose Estimation, Multi-Modal Fusion, Real-Time Surveillance, Deep Learning},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712742}
  }