  In this paper we propose a novel image representation called face X-ray for
detecting forgery in face images. The face X-ray of an input face image is a
greyscale image that reveals whether the input image can be decomposed into the
blending of two images from different sources. It does so by showing the
blending boundary for a forged image and the absence of blending for a real
image. We observe that most existing face manipulation methods share a common
step: blending the altered face into an existing background image. For this
reason, face X-ray provides an effective way for detecting forgery generated by
most existing face manipulation algorithms. Face X-ray is general in the sense
that it only assumes the existence of a blending step and does not rely on any
knowledge of the artifacts associated with a specific face manipulation
technique. Indeed, the algorithm for computing face X-ray can be trained
without fake images generated by any of the state-of-the-art face manipulation
methods. Extensive experiments show that face X-ray remains effective when
applied to forgery generated by unseen face manipulation techniques, while most
existing face forgery detection or deepfake detection algorithms experience a
significant performance drop.
