  In this work, we present a generalized and robust facial manipulation
detection method based on color distribution analysis of the vertical region of
edge in a manipulated image. Most of the contemporary facial manipulation
method involves pixel correction procedures for reducing awkwardness of pixel
value differences along the facial boundary in a synthesized image. For this
procedure, there are distinctive differences in the facial boundary between
face manipulated image and unforged natural image. Also, in the forged image,
there should be distinctive and unnatural features in the gap distribution
between facial boundary and background edge region because it tends to damage
the natural effect of lighting. We design the neural network for detecting
face-manipulated image with these distinctive features in facial boundary and
background edge. Our extensive experiments show that our method outperforms
other existing face manipulation detection methods on detecting synthesized
face image in various datasets regardless of whether it has participated in
training.
