Client-Side Virtual Camera Impersonation Attacks Detection on Automatic Proctoring Exam

Online proctored exams require authentication and face verification before students access the exams. However, using an impersonation attack model, students can bypass the face verification phase using a virtual webcam. We aim to propose an approach that detects cameras based on their Hardware ID. In addition, our approach includes a log engine that collects device information, such as the total number of webcams and the number of webcams in the student device. This detection model prevents impersonation attacks on online proctoring and runs on the client-side browser. Based on our experiment, our suggested approach can detect virtual cameras on students’ devices during user verification and online-proctoring examinations by integrating client-side virtual webcam detection.