  A larger portion of fake news quotes untampered images from other sources
with ulterior motives rather than conducting image forgery. Such elaborate
engraftments keep the inconsistency between images and text reports stealthy,
thereby, palm off the spurious for the genuine. This paper proposes an
architecture named News Image Steganography (NIS) to reveal the aforementioned
inconsistency through image steganography based on GAN. Extractive
summarization about a news image is generated based on its source texts, and a
learned steganographic algorithm encodes and decodes the summarization of the
image in a manner that approaches perceptual invisibility. Once an encoded
image is quoted, its source summarization can be decoded and further presented
as the ground truth to verify the quoting news. The pairwise encoder and
decoder endow images of the capability to carry along their imperceptible
summarization. Our NIS reveals the underlying inconsistency, thereby, according
to our experiments and investigations, contributes to the identification
accuracy of fake news that engrafts untampered images.
