  Concern regarding the wide-spread use of fraudulent images/videos in social
media necessitates precise detection of such fraud. The importance of facial
expressions in communication is widely known, and adversarial attacks often
focus on manipulating the expression related features. Thus, it is important to
develop methods that can detect manipulations in facial expressions, and
localize the manipulated regions. To address this problem, we propose a
framework that is able to detect manipulations in facial expression using a
close combination of facial expression recognition and image manipulation
methods. With the addition of feature maps extracted from the facial expression
recognition framework, our manipulation detector is able to localize the
manipulated region. We show that, on the Face2Face dataset, where there is
abundant expression manipulation, our method achieves over 3% higher accuracy
for both classification and localization of manipulations compared to
state-of-the-art methods. In addition, results on the NeuralTextures dataset
where the facial expressions corresponding to the mouth regions have been
modified, show 2% higher accuracy in both classification and localization of
manipulation. We demonstrate that the 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, thus ensuring generalizability of the approach.
