Global Texture Enhancement for Fake Face Detection in the Wild
- URL: http://arxiv.org/abs/2002.00133v3
- Date: Thu, 19 Mar 2020 00:54:37 GMT
- Title: Global Texture Enhancement for Fake Face Detection in the Wild
- Authors: Zhengzhe Liu, Xiaojuan Qi, Philip Torr
- Abstract summary: We propose a new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection.
Experimental results on several datasets demonstrate that our Gram-Net outperforms existing approaches.
- Score: 43.556943969290636
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative Adversarial Networks (GANs) can generate realistic fake face
images that can easily fool human beings.On the contrary, a common
Convolutional Neural Network(CNN) discriminator can achieve more than 99.9%
accuracyin discerning fake/real images. In this paper, we conduct an empirical
study on fake/real faces, and have two important observations: firstly, the
texture of fake faces is substantially different from real ones; secondly,
global texture statistics are more robust to image editing and transferable to
fake faces from different GANs and datasets. Motivated by the above
observations, we propose a new architecture coined as Gram-Net, which leverages
global image texture representations for robust fake image detection.
Experimental results on several datasets demonstrate that our Gram-Net
outperforms existing approaches. Especially, our Gram-Netis more robust to
image editings, e.g. down-sampling, JPEG compression, blur, and noise. More
importantly, our Gram-Net generalizes significantly better in detecting fake
faces from GAN models not seen in the training phase and can perform decently
in detecting fake natural images.
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