  With the proliferation of deep generative models, deepfakes are improving in
quality and quantity everyday. However, there are subtle authenticity signals
in pristine videos, not replicated by SOTA GANs. We contrast the movement in
deepfakes and authentic videos by motion magnification towards building a
generalized deepfake source detector. The sub-muscular motion in faces has
different interpretations per different generative models which is reflected in
their generative residue. Our approach exploits the difference between real
motion and the amplified GAN fingerprints, by combining deep and traditional
motion magnification, to detect whether a video is fake and its source
generator if so. Evaluating our approach on two multi-source datasets, we
obtain 97.17% and 94.03% for video source detection. We compare against the
prior deepfake source detector and other complex architectures. We also analyze
the importance of magnification amount, phase extraction window, backbone
network architecture, sample counts, and sample lengths. Finally, we report our
results for different skin tones to assess the bias.
