  Visual content has become the primary source of information, as evident in
the billions of images and videos, shared and uploaded on the Internet 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 based DeepFakes are hard to
detect. Reenactment alterations allow the source to change the target
expressions and create photo-realistic images and videos. While technology can
be potentially used for several applications, the malicious usage of automatic
reenactment has a very large social implication. It is therefore important to
develop detection techniques to distinguish real images and videos with the
altered ones. 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.
