• Ms. R Varshini
• Ms. Aishwaryaa Lakshmi VM
• Mrs. Sindhu N

Academic dishonesty which includes malpractice and plagiarism, is a major issue in educational institutions worldwide and is particularly prevalent in entrance exams, certification exams, and university exams. Traditional methods of detecting malpractice such as manual monitoring by invigilators, are often inadequate and can be time-consuming and expensive. In this paper, we present a malpractice detection system for use in exam halls that leverages machine learning algorithms and computer vision libraries to detect instances of malpractice and other forms of academic dishonesty. The system uses video monitoring to identify suspicious behavior by test takers. Our system gives us the possibility to enhance the integrity of high-stakes exams and maintain a level playing field for all test subjects by automatically detecting malpractice in exam rooms.

Bounding boxes, deep learning, malpractice, object detection, twilio, YOLOv5.

"REAL-TIME MALPRACTICE DETECTION IN EXAM HALLS BY ENFORCING MINIMUM DISTANCE SEATING ARRANGEMENT USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.e188-e192, May-2023, Available :http://www.jetir.org/papers/JETIR2305428.pdf

"REAL-TIME MALPRACTICE DETECTION IN EXAM HALLS BY ENFORCING MINIMUM DISTANCE SEATING ARRANGEMENT USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppe188-e192, May-2023, Available at : http://www.jetir.org/papers/JETIR2305428.pdf