  This paper presents a generalized and robust face manipulation detection
method based on the edge region features appearing in images. Most contemporary
face synthesis processes include color awkwardness reduction but damage the
natural fingerprint in the edge region. In addition, these color correction
processes do not proceed in the non-face background region. We also observe
that the synthesis process does not consider the natural properties of the
image appearing in the time domain. Considering these observations, we propose
a facial forensic framework that utilizes pixel-level color features appearing
in the edge region of the whole image. Furthermore, our framework includes a
3D-CNN classification model that interprets the extracted color features
spatially and temporally. Unlike other existing studies, we conduct
authenticity determination by considering all features extracted from multiple
frames within one video. Through extensive experiments, including real-world
scenarios to evaluate generalized detection ability, we show that our framework
outperforms state-of-the-art facial manipulation detection technologies in
terms of accuracy and robustness.
