Automated face swapping and its detection

Face has been used as one of the mainstream manners for user identification. However, with the popularity of face-swapping apps, it takes only a few seconds to change the faces between two facial images. Such swapped results, when using improperly or carelessly, might create some security issues in certain applications. This paper is the first work to address the importance of this issue and discusses the feasibility to achieve an automated face swapping detection through machine learning. Several approaches are tested on a face swapping database derived from a face benchmarking repository. The best solution in the experiments achieved a detection accuracy of over 92%.