Integrating Deep Learning Models to Improve Cheating Detection in UPOU's Specialized Online Assessment Platform. Accepted to the 26th Philippine Computing Science Congress, 2026.
Rule-based proctoring struggles with subtle behavioral cues. I extracted facial landmarks, eyelid features, head pose and object-detection signals from the Online Exam Proctoring dataset into windowed time-series features, then trained and benchmarked five architectures (CNN1D, LSTM, BiGRU, TCN, CNN-LSTM) with subject-wise five-fold cross-validation and nested hyperparameter tuning. Every model beat the SOAP baseline; CNN-LSTM led overall.
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Feature pipeline
Built the extraction pipeline turning raw proctoring video into normalized, windowed behavior sequences for training.
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Five architectures
Engineered and benchmarked CNN, LSTM, BiGRU, TCN and CNN-LSTM with a faithful recreation of SOAP's rule-based baseline as the comparison point.
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