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 than99.9%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-Netoutperforms existing approaches. Especially, our Gram-Netis more robust to image editings, e.g. down-sampling, JPEGcompression, blur, and noise.
Original language | English |
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Title of host publication | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 : Proceedings |
Pages | 8057-8066 |
Number of pages | 10 |
ISBN (Electronic) | 9781728171685 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States Duration: 14 Jun 2020 → 19 Jun 2020 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Publisher | IEEE Computer Society |
ISSN (Print) | 1063-6919 |
Conference
Conference | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 14/06/20 → 19/06/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
This work was supported by the ERC grant ERC-2012-AdG 321162-HELIOS, EPSRC grant Seebibyte EP/M013774/1 and EPSRC/MURI grant EP/N019474/1.