Name
Title | ||
|---|---|---|
CGR-GAN: CG facial image regeneration for anti-forensics based on generative adversarial network |
Abstract | ||
|---|---|---|
In this paper, a Computer-generated graphics (CG) facial image regeneration scheme for anti-forensics based on generative adversarial network (CGR-GAN) is proposed. The generator of CGR-GAN utilizes a deep U-Net structure, and its discriminator utilizes some stacked convolution layers. Besides, content loss and style loss are both designed to guarantee that the regenerated CG facial images (CGR) retain both the facial profile of the original CG and the characteristics of natural image (NI). Experimental results and analysis demonstrate that the CG facial images regenerated by the proposed anti-forensics scheme can achieve better visual quality compared with those of the existing CG facial image anti-forensics and domain adaptation methods, and it can strike a good balance between visual quality and deception ability. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMM.2019.2959443 | IEEE Transactions on Multimedia |
Keywords | DocType | Volume |
Feature extraction,Detectors,Forensics,Image color analysis,Convolution,Histograms,Generative adversarial networks | Journal | 22 |
Issue | ISSN | Citations |
10 | 1520-9210 | 2 |
PageRank | References | Authors |
0.39 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Peng | 1 | 360 | 38.79 |
| Li-Ping Yin | 2 | 2 | 0.39 |
| Le-Bing Zhang | 3 | 13 | 2.63 |
| Min Long | 4 | 179 | 23.63 |