Multiple Instance Learning for Cheating Detection and Localization in Online Examinations

Abstract

The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection. In this article, we develop and present CHEESE, a CHEating detection framework via multiple instance learning. The framework consists of a label generator that implements weak supervision and a feature encoder to learn discriminative features. In addition, the framework combines body posture and background features extracted by 3-D convolution with eye gaze, head posture, and facial features captured by OpenFace 2.0. These features are fed into the spatiotemporal graph module by stitching to analyze the spatiotemporal changes in video clips to detect the cheating behaviors. Our experiments on three datasets, University of Central Florida (UCF)-Crime, ShanghaiTech, and online exam proctoring (OEP), prove the effectiveness of our method as compared to the state-of-the-art approaches and obtain the frame-level area under the curve (AUC) score of 87.58% on the OEP dataset.