Detecting Face2Face Facial Reenactment in Videos

Abstract

Visual content has become the primary source of information, as evident in the billions of images and videos, shared and uploaded every single day. This has led to an increase in alterations in images and videos to make them more informative and eye-catching for the viewers worldwide. Some of these alterations are simple, like copy-move, and are easily detectable, while other sophisticated alterations like reenactment are hard to detect. Reenactment alterations allow the source to change the target expressions and create photo-realistic images and videos where such modifications are hard to detect. Significant work has been done towards creating such images and videos. However, the detection of such alterations still requires research. This research proposes a learning-based algorithm for detecting reenactment based alterations. The proposed algorithm uses a multi-stream network that learns regional artifacts and provides a robust performance at various compression levels. We also propose a loss function for the balanced learning of the streams for the proposed network. The performance is evaluated on the publicly available FaceForensics dataset. The results show state-of-the-art classification accuracy of 99.96%, 99.10%, and 91.20% for no, easy, and hard compression factors, respectively.