CGR-GAN: CG Facial Image Regeneration for Antiforensics Based on Generative Adversarial Network | IEEE Journals & Magazine | IEEE Xplore

CGR-GAN: CG Facial Image Regeneration for Antiforensics 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 g...Show More

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.
Published in: IEEE Transactions on Multimedia ( Volume: 22, Issue: 10, October 2020)
Page(s): 2511 - 2525
Date of Publication: 12 December 2019

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