Information Security of Educational Portal Based on Face Anti-Spoofing Method: Effectiveness of Tiny Neural Network Machine Learning Model

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.