  Distinguishing between computer-generated (CG) and natural photographic (PG)
images is of great importance to verify the authenticity and originality of
digital images. However, the recent cutting-edge generation methods enable high
qualities of synthesis in CG images, which makes this challenging task even
trickier. To address this issue, a joint learning strategy with deep texture
and high-frequency features for CG image detection is proposed. We first
formulate and deeply analyze the different acquisition processes of CG and PG
images. Based on the finding that multiple different modules in image
acquisition will lead to different sensitivity inconsistencies to the
convolutional neural network (CNN)-based rendering in images, we propose a deep
texture rendering module for texture difference enhancement and discriminative
texture representation. Specifically, the semantic segmentation map is
generated to guide the affine transformation operation, which is used to
recover the texture in different regions of the input image. Then, the
combination of the original image and the high-frequency components of the
original and rendered images are fed into a multi-branch neural network
equipped with attention mechanisms, which refines intermediate features and
facilitates trace exploration in spatial and channel dimensions respectively.
Extensive experiments on two public datasets and a newly constructed dataset
with more realistic and diverse images show that the proposed approach
outperforms existing methods in the field by a clear margin. Besides, results
also demonstrate the detection robustness and generalization ability of the
proposed approach to postprocessing operations and generative adversarial
network (GAN) generated images.
