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May 16, 2026Frontiers in Big DataOpen Access

Cheatomaly: weakly supervised video anomaly ranking for exam cheating detection using vision transformers

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Authors

EMEl Mehdi Alaoui MraniABAnas BouayadKFKhalid Fardousse

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Overview

Randomized trial demonstrates effective video anomaly detection in classroom examinations, suggesting a new benchmark for capturing subtle cheating behaviors.

Key Points

  • This research aims to develop a method for detecting cheating in classroom examinations by utilizing video analysis and weak supervision.
  • Introduced Cheatomaly, a curated video dataset for examining cheating behavior.
  • Utilized multiple instance learning with vision transformer features to rank videos based on anomaly.
  • Implemented a margin-based ranking objective focusing on segment-level representations.
  • Achieved strong video-level discrimination and effective frame-level localization in analyses.
  • Showed that temporal aggregation impacts the balance between ranking and localization without consistent gains.
  • Highlighted challenges in modeling context-dependent temporal behavior during anomaly detection.

Cite This Study

Mrani et al. (2026) studied this question.

synapsesocial.com/papers/6a0808afa487c87a6a40b009https://doi.org/10.3389/fdata.2026.1817120
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