  The rapid progress in the ease of creating and spreading ultra-realistic
media over social platforms calls for an urgent need to develop a generalizable
deepfake detection technique. It has been observed that current deepfake
generation methods leave discriminative artifacts in the frequency spectrum of
fake images and videos. Inspired by this observation, in this paper, we present
a novel approach, termed as MD-CSDNetwork, for combining the features in the
spatial and frequency domains to mine a shared discriminative representation
for classifying \textit{deepfakes}. MD-CSDNetwork is a novel cross-stitched
network with two parallel branches carrying the spatial and frequency
information, respectively. We hypothesize that these multi-domain input data
streams can be considered as related supervisory signals. The supervision from
both branches ensures better performance and generalization. Further, the
concept of cross-stitch connections is utilized where they are inserted between
the two branches to learn an optimal combination of domain-specific and shared
representations from other domains automatically. Extensive experiments are
conducted on the popular benchmark dataset namely FaceForeniscs++ for forgery
classification. We report improvements over all the manipulation types in
FaceForensics++ dataset and comparable results with state-of-the-art methods
for cross-database evaluation on the Celeb-DF dataset and the Deepfake
Detection Dataset.
