Detection and Localization of Facial Expression Manipulations

Concerns regarding the wide-spread use of forged images and videos in social media necessitate precise detection of such fraud. Facial manipulations can be created by Identity swap (DeepFake) or Expression swap. Contrary to the identity swap, which can easily be detected with novel deepfake detection methods, expression swap detection has not yet been addressed extensively. The importance of facial expressions in inter-person communication is known. Consequently, it is important to develop methods that can detect and localize manipulations in facial expressions. To this end, we present a novel framework to exploit the underlying feature representations of facial expressions learned from expression recognition models to identify the manipulated features. Using discriminative feature maps extracted from a facial expression recognition framework, our manipulation detector is able to localize the manipulated regions of input images and videos. On the Face2Face dataset, (abundant expression manipulation), and Neural-Textures dataset (facial expressions manipulation corresponding to the mouth regions), our method achieves higher accuracy for both classification and localization of manipulations compared to state-of-the-art methods. Furthermore, we demonstrate that our method performs at-par with the state-of-the-art methods in cases where the expression is not manipulated, but rather the identity is changed, leading to a generalized approach for facial manipulation detection.