AI Powered Malpractice Detection System for Exam

This project introduces an automated Examination Malpractice Detection System that utilizes YOLOv8 for real-time

object detection and MediaPipe for human pose estimation. By processing live video streams through a Flask-based backend, the

system identifies prohibited items and suspicious behavioral patterns such as excessive head movement or unauthorized

communication. To ensure high reliability, it employs a 60% confidence threshold and a decision-fusion layer that reduces false

positives by analyzing sequences of motion. The architecture features a seamless administrative response layer that captures

time-stamped evidence and compiles infractions into a secure zip archive. Upon session completion, the system automatically

transmits a comprehensive summary and evidence file to examiners via an integrated email notification service, ensuring a

transparent and objective disciplinary review process.