  The creation of manipulated multimedia content involving human characters has
reached in the last years unprecedented realism, calling for automated
techniques to expose synthetically generated faces in images and videos. This
work explores the analysis of spatio-temporal texture dynamics of the video
signal, with the goal of characterizing and distinguishing real and fake
sequences. We propose to build a binary decision on the joint analysis of
multiple temporal segments and, in contrast to previous approaches, to exploit
the textural dynamics of both the spatial and temporal dimensions. This is
achieved through the use of Local Derivative Patterns on Three Orthogonal
Planes (LDP-TOP), a compact feature representation known to be an important
asset for the detection of face spoofing attacks. Experimental analyses on
state-of-the-art datasets of manipulated videos show the discriminative power
of such descriptors in separating real and fake sequences, and also identifying
the creation method used. Linear Support Vector Machines (SVMs) are used which,
despite the lower complexity, yield comparable performance to previously
proposed deep models for fake content detection.
