Face Morphing Attacks: A Threat to eLearning?

Recently, the use of remote education via electronic media, i.e. eLearning, has increased due to the COVID-19 pandemic. To achieve secure and reliable identity verification in eLearning exams, it has been suggested to employ face recognition technologies for remote student authentication in online examinations. However, novel attacks on face recognition in eLearning systems have rarely been considered in the scientific literature.In this work, we investigate the feasibility of so-called face morphing attacks in eLearning systems. Such attacks can be launched in scenarios where students are authenticated via identity documents containing face images that are remotely presented prior to online examinations. In this relevant scenario, students are able to fool human examination and automated face recognition by morphing their face image with that of an accomplice, e.g. fellow student. Resulting morphed face images contain biometric information of both subjects contributing to it. Consequentially, an accomplice could take part in an online examination for a student with high probability of passing an identity verification unnoticed. We assess the vulnerability of a commercial and an open-source face recognition system to said attack. To this end, a realistic dataset of morphing attacks is collected. It is shown that automated face recognition in eLearning systems can be tricked with alarmingly high success chance.