Machine learning based inference privacy sanitization for online proctoring - Chen, Xinyu (Nanyang Technological University, 2022)
The detection of cheating behavior is the key direction of online proctoring research, but the privacy protection of candidates is often neglected, resulting in the risk of personal information leakage of candidates in proctored video streams. In recent years, how to sanitize the privacy in online invigilation videos without affecting the accuracy of examinee identity and cheating behavior identification has become a very important research topic. In this work, we have studied the current advanced privacy protection methods, combined with the particularity of online proctoring scenarios, and proposed new online exam cheating detection process, using high-resolution background matting and human body key point recognition method to complete the protection of candidates' privacy. In addition, we apply a state-of-the-art AlphaAction model to complete the detection of cheating behavior. Then we test the above methods comprehensively by using our own recorded videos of simulated online exam cheating. The final experimental evaluation results show that the privacy protection method adopted in this dissertation is effective and will not affect the accuracy of subsequent cheating detection, and it also provides valuable insight for future study on online proctoring privacy protection.