Abstract:
This article presents the implementation of a machine learning-based face anti-spoofing method to enhance
the security of an educational information portal for university students. The study addresses the challenge of
preventing academic dishonesty by ensuring that only authorized individuals can complete intermediate and final
assessment tasks. The proposed method leverages the Tiny neural network model, selected for its efficiency in compact
data processing, alongside the dlib system in Python and the LCC_FASD dataset, which enables precise detection of 68
facial landmarks. Using a confusion matrix to evaluate performance, the method achieved a 94.47% accuracy in
detecting spoofing attempts. These findings demonstrate the effectiveness of the proposed approach in safeguarding
educational platforms and maintaining academic integrity.
