  With the powerful deep network architectures, such as generative adversarial
networks, one can easily generate photorealistic images. Although the generated
images are not dedicated for fooling human or deceiving biometric
authentication systems, research communities and public media have shown great
concerns on the security issues caused by these images. This paper addresses
the problem of identifying deep network generated (DNG) images. Taking the
differences between camera imaging and DNG image generation into
considerations, we analyze the disparities between DNG images and real images
in different color components. We observe that the DNG images are more
distinguishable from real ones in the chrominance components, especially in the
residual domain. Based on these observations, we propose a feature set to
capture color image statistics for identifying DNG images. Additionally, we
evaluate several detection situations, including the training-testing data are
matched or mismatched in image sources or generative models and detection with
only real images. Extensive experimental results show that the proposed method
can accurately identify DNG images and outperforms existing methods when the
training and testing data are mismatched. Moreover, when the GAN model is
unknown, our methods also achieves good performance with one-class
classification by using only real images for training.
