Multiple Instance Learning for Cheating Detection and Localization in
  Online Examinations
Yemeng Liu Jing Ren Jianshuo Xu Xiaomei Bai Roopdeep Kaur Feng Xia
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 paper, 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 3D convolution with eye gaze,
head posture and facial features captured by OpenFace 2.0. These features are
fed into the spatio-temporal graph module by stitching to analyze the
spatio-temporal changes in video clips to detect the cheating behaviors. Our
experiments on three datasets, 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 AUC score of 87.58% on
the OEP dataset.