A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 6; pp. 3527 - 3538
• Main Authors: Chen, Beijing, Liu, Xin, Zheng, Yuhui, Zhao, Guoying, Shi, Yun-Qing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blurring; Color; color space; Convolution; Convolutional neural networks; Face detection; Face recognition; Faces; Feature extraction; Generated face; generative adversarial network; Generative adversarial networks; Image color analysis; Image compression; Modules; Multilayers; Performance degradation; Robustness; Xception
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2021.3116679

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.