  With the development of computer graphics technology, the images synthesized
by computer software become more and more closer to the photographs. While
computer graphics technology brings us a grand visual feast in the field of
games and movies, it may also be utilized by someone with bad intentions to
guide public opinions and cause political crisis or social unrest. Therefore,
how to distinguish the computer-generated graphics (CG) from the photographs
(PG) has become an important topic in the field of digital image forensics.
This paper proposes a dual stream convolutional neural network based on channel
joint and softpool. The proposed network architecture includes a residual
module for extracting image noise information and a joint channel information
extraction module for capturing the shallow semantic information of image. In
addition, we also design a residual structure to enhance feature extraction and
reduce the loss of information in residual flow. The joint channel information
extraction module can obtain the shallow semantic information of the input
image which can be used as the information supplement block of the residual
module. The whole network uses SoftPool to reduce the information loss of
down-sampling for image. Finally, we fuse the two flows to get the
classification results. Experiments on SPL2018 and DsTok show that the proposed
method outperforms existing methods, especially on the DsTok dataset. For
example, the performance of our model surpasses the state-of-the-art by a large
margin of 3%.
