  Deepfake techniques have been widely used for malicious purposes, prompting
extensive research interest in developing Deepfake detection methods. Deepfake
manipulations typically involve tampering with facial parts, which can result
in inconsistencies across different parts of the face. For instance, Deepfake
techniques may change smiling lips to an upset lip, while the eyes remain
smiling. Existing detection methods depend on specific indicators of forgery,
which tend to disappear as the forgery patterns are improved. To address the
limitation, we propose Mover, a new Deepfake detection model that exploits
unspecific facial part inconsistencies, which are inevitable weaknesses of
Deepfake videos. Mover randomly masks regions of interest (ROIs) and recovers
faces to learn unspecific features, which makes it difficult for fake faces to
be recovered, while real faces can be easily recovered. Specifically, given a
real face image, we first pretrain a masked autoencoder to learn facial part
consistency by dividing faces into three parts and randomly masking ROIs, which
are then recovered based on the unmasked facial parts. Furthermore, to maximize
the discrepancy between real and fake videos, we propose a novel model with
dual networks that utilize the pretrained encoder and masked autoencoder,
respectively. 1) The pretrained encoder is finetuned for capturing the encoding
of inconsistent information in the given video. 2) The pretrained masked
autoencoder is utilized for mapping faces and distinguishing real and fake
videos. Our extensive experiments on standard benchmarks demonstrate that Mover
is highly effective.
