Paper Info
Title
A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception
Abstract
In recent years, generative adversarial networks (GANs) have been widely used to generate realistic fake face images, which can easily deceive human beings. To detect these images, some methods have been proposed. However, their detection performance will be degraded greatly when the testing samples are post-processed. In this paper, some experimental studies on detecting post-processed GAN-generated face images find that (a) both the luminance component and chrominance components play an important role, and (b) the RGB and YCbCr color spaces achieve better performance than the HSV and Lab color spaces. Therefore, to enhance the robustness, both the luminance component and chrominance components of dual-color spaces (RGB and YCbCr) are considered to utilize color information effectively. In addition, the convolutional block attention module and multilayer feature aggregation module are introduced into the Xception model to enhance its feature representation power and aggregate multilayer features, respectively. Finally, a robust dual-stream network is designed by integrating dual-color spaces RGB and YCbCr and using an improved Xception model. Experimental results demonstrate that our method outperforms some existing methods, especially in its robustness against different types of post-processing operations, such as JPEG compression, Gaussian blurring, gamma correction, and median filtering.
Year
DOI
Venue
2022
10.1109/TCSVT.2021.3116679
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Generated face,generative adversarial network,Xception,color space
Journal
32
Issue
ISSN
Citations 
6
1051-8215
1
PageRank 
References 
Authors
0.41
15
5
Name
Order
Citations
PageRank
Beijing Chen130415.72
Xin Liu23919320.56
Yuhui Zheng329016.45
Guoying Zhao411.09
Yun Qing Shi551823.34