  It has become increasingly challenging to distinguish real faces from their
visually realistic fake counterparts, due to the great advances of deep
learning based face manipulation techniques in recent years. In this paper, we
introduce a deep learning method to detect face manipulation. It consists of
two stages: feature extraction and binary classification. To better distinguish
fake faces from real faces, we resort to the triplet loss function in the first
stage. We then design a simple linear classification network to bridge the
learned contrastive features with the real/fake faces. Experimental results on
public benchmark datasets demonstrate the effectiveness of this method, and
show that it generates better performance than state-of-the-art techniques in
most cases.
