Swapping Face Images Based on Augmented Facial Landmarks and Its Detection

Abstract

Facial landmark points that are precisely extracted from the face images improve the performance of many applications in the domains of computer vision and graphics. Face swapping is one of such applications. With the availability of sophisticated image editing tools and the use of deep learning models, it is easy to create swapped face images or face swap attacks in images or videos even for non-professionals. Face swapping transfers a face from a source to a destination image, while preserving photo realism. It has potential applications in computer games, privacy protection, etc. However, it could also be used for fraudulent purposes. In this paper, we propose an approach to create face swap attacks and detect them from the original images. The augmented 81-facial landmark points are extracted for creating the face swap attacks. The feature descriptors Weighted Local Magnitude Patterns (WLMP) and Support Vector Machines (SVM) are utilized for the swapped face images detection. The performance of the proposed approach is demonstrated by different types of SVM classifiers on a real-world dataset. Experimental results show that the proposed system effectively does face swapping and detection with an accuracy of 95%.