Generation Model of Gender-forged Face Image Based on Improved CycleGAN

Abstract

Deepfake can be used to combine human voices,faces and body movements into fake content,switch gender and change age,etc.There are some problems of gender-forged face images based on generative adversarial image translation networks such as the irrelevant image domain changes easily and insufficient face details in generated images.To solve these problems,an gene-ration model of gender-forged face image based on improved CycleGAN is proposed.Firstly,the generator is optimized by using the attention mechanism and adaptive residual blocks to extract richer facial features.Then,with the aim to improve the ability of the discriminator,the loss function is modified by the idea of relative loss.Finally,a model training strategy based on age constraints is proposed to reduce the impact of age changes on the generated images.Performing experiments on the CelebA and IMDB-WIKI datasets,the experimental results show that,compared with the original CycleGAN method and the UGATIT method,theproposed method can generate more real gender-forged face images.The average content accuracy of fake male images and fake female images is 82.65% and 78.83%,and the average FID score is 32.14 and 34.50,respectively.

Keywords

• deepfake|deep learning|generative adversarial network|image translation|image generation|facial gender forgery