  Deep-learning-based technologies such as deepfakes ones have been attracting
widespread attention in both society and academia, particularly ones used to
synthesize forged face images. These automatic and professional-skill-free face
manipulation technologies can be used to replace the face in an original image
or video with any target object while maintaining the expression and demeanor.
Since human faces are closely related to identity characteristics, maliciously
disseminated identity manipulated videos could trigger a crisis of public trust
in the media and could even have serious political, social, and legal
implications. To effectively detect manipulated videos, we focus on the
position offset in the face blending process, resulting from the forced affine
transformation of the normalized forged face. We introduce a method for
detecting manipulated videos that is based on the trajectory of the facial
region displacement. Specifically, we develop a virtual-anchor-based method for
extracting the facial trajectory, which can robustly represent displacement
information. This information was used to construct a network for exposing
multidimensional artifacts in the trajectory sequences of manipulated videos
that is based on dual-stream spatial-temporal graph attention and a gated
recurrent unit backbone. Testing of our method on various manipulation datasets
demonstrated that its accuracy and generalization ability is competitive with
that of the leading detection methods.
