Applying Benford's Law as an Efficient and Low-cost Solution for Verifying the Authenticity of Users' Video Streams in Learning Management Systems

An important challenge of online learning management systems (LMS) relates to continuously verifying the identity of students even after they have successfully authenticated. Although various continuous user identification solutions exist, they are rather focused on complex examination proctoring systems. Challenges further increase within large-scale online courses, which require a strong infrastructure to support numerous real-time video streams for verifying the identity of students. Considering that the students' input video stream is an important factor for verifying their identity, and given that naturally generated data streams have been found to adhere to a pre-defined behavior as indicated by the Benford's law, in this work we investigate whether Benford's law can be applied as a reliable, efficient and cost-effective method for the detection of authentic vs. pre-recorded input video streams during continuous students' identity verification within online LMS. In doing so, we suggest a prediction model based on the distribution of the first digits of image Discrete Cosine Transform (DCT) coefficients from the students' input video stream. We found that the input video stream type (authentic vs. pre-recorded) can be inferred within a few seconds in real-time. A system performance evaluation indicates that the suggested model can support up to 1000 concurrent online students using a conventional and low-cost server setup and architecture.