  Making computer-generated (CG) images more difficult to detect is an
interesting problem in computer graphics and security. While most approaches
focus on the image rendering phase, this paper presents a method based on
increasing the naturalness of CG facial images from the perspective of spoofing
detectors. The proposed method is implemented using a convolutional neural
network (CNN) comprising two autoencoders and a transformer and is trained
using a black-box discriminator without gradient information. Over 50% of the
transformed CG images were not detected by three state-of-the-art spoofing
detectors. This capability raises an alarm regarding the reliability of facial
authentication systems, which are becoming widely used in daily life.
