Multiple Instance Learning for Cheating Detection and Localization in Online Examinations
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916118791192576 |
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| author | Liu, Yemeng Ren, Jing Xu, Jianshuo Bai, Xiaomei Kaur, Roopdeep Xia, Feng |
| author_facet | Liu, Yemeng Ren, Jing Xu, Jianshuo Bai, Xiaomei Kaur, Roopdeep Xia, Feng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiple Instance Learning for Cheating Detection and Localization in Online Examinations Liu, Yemeng Ren, Jing Xu, Jianshuo Bai, Xiaomei Kaur, Roopdeep Xia, Feng Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning 68T40, 68T45 I.2.10; I.5.4 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. |
| title | Multiple Instance Learning for Cheating Detection and Localization in Online Examinations |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning 68T40, 68T45 I.2.10; I.5.4 |
| url | https://arxiv.org/abs/2402.06107 |