Published Paper Details:

Online examinations and remote coding assessments require proctoring systems that can verify candidate authenticity, detect suspicious behaviour, preserve evidence and support fair human review. This paper presents a multimodal AI-based online proctoring system designed for secure coding assessments. The proposed system integrates captcha-less behavioural login analysis, OTP verification, a browser-based coding interface, MTCNN face analysis, MediaPipe FaceMesh gaze estimation, YOLOv8 object detection, audio activity monitoring, cooldown-based violation aggregation and automated integrity- report generation. Unlike conventional webcam-only monitoring, the system represents a candidate session as a multimodal stream and applies mathematical decision rules to authentication, visual attention, object presence, audio activity and event acceptance. The implementation is analysed using project source code and generated violation logs. A case-study session containing 100 accepted events demonstrates the complete monitoring pipeline and shows that gaze deviation, face absence, multiple-face presence and prohibited-object events can be captured in a structured evidence model. The paper further discusses computational complexity, deployment security, privacy, limitations and validation requirements for institutional use.