Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 31-39.doi: 10.11896/jsjkx.210600012

• Computer Vision: Theory and Application • Previous Articles     Next Articles

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

SHI Da, LU Tian-liang, DU Yan-hui, ZHANG Jian-ling, BAO Yu-xuan   

  1. College of Information and Cyber Security,People's Public Security University of China,Beijing 100038,China
  • Received:2021-06-01 Revised:2021-09-06 Online:2022-02-15 Published:2022-02-23
  • About author:SHI Da,born in 1997,master.His main research interests include cyber security and artificial intelligence.
    LU Tian-liang,born in 1985,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include cyber security and artificial intelligence.
  • Supported by:
    National Key R & D Program of China(2017YFB0802804) and 2020 Fundamental Research Funds for the Central Universities of PPSUC(2020JKF101).

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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.

Key words: Deep learning, Deepfake, Facial gender forgery, Generative adversarial network, Image generation, Image translation

CLC Number: 

  • TP309
[1]BAO Y X,LU T L,DU Y H.Overview of Deepfake Video Detection Technology[J].Computer Science,2020,47(9):283-292.
[2]Deepfakes[OL].https://github.com/deepfakes/faceswap.
[3]FaceSwap[OL].https://github.com/MarekKowalski/FaceSwap/.
[4]THIES J,ZOLLHOFER M,STAMMINGER M,et al.Face2-face:Real-time face capture and reenactment of rgb videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:2387-2395.
[5]Faceswap-GAN[OL].https://github.com/shaoanlu/faceswap-GAN.
[6]ISOLA P,ZHU J Y,ZHOU T,et al.Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1125-1134.
[7]ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Vomputer vision.2017:2223-2232.
[8]KIM J,KIM M,KANG H,et al.U-GAT-IT:unsupervised ge-nerative attentional networks with adaptive layer-instance normalization for image-to-image translation[J].arXiv:1907.10830,2019.
[9]WANG Z,TANG X,LUO W,et al.Face aging with identity-preserved conditional generative adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7939-7947.
[10]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein ge-nerative adversarial networks[C]//International Conference on Machine Learning.PMLR,2017:214-223.
[11]MAO X,LI Q,XIE H,et al.Least squares generative adversarial networks[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.2017:2794-2802.
[12]GULRAJANI I,AHMES F,ARJOVSKY M,et al.Improvedtraining of wasserstein gans[J].arXiv:1704.00028,2017.
[13]UPCHURCH P,GARDNER J,PLEISS G,et al.Deep feature interpolation for image content changes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7064-7073.
[14]PERARNAU G,VAN DE WEIJER J,et al.Invertible conditio-nal gans for image editing[J].arXiv:1611.06355,2016.
[15]WANG S M,LI S F.Multi-domain image conversion methodbased on feature vector transformation GAN[J].Journal of Yunnan University(Natural Sciences Edition),2020,42(6):1080-1090.
[16]LIU M Y,BREUEL T,KAUTZ J.Unsupervised image-to-image translation networks[J].arXiv:1703.00848,2017.
[17]HUANG X,LIU M Y,BELONGIE S,et al.Multimodal unsupervised image-to-image translation[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:172-189.
[18]PARK D Y,LEE K H.Arbitrary style transfer with style-attentional networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:5880-5888.
[19]XIAO T,HONG J,MA J.Elegant:Exchanging latent encodings with gan for transferring multiple face attributes[C]//Procee-dings of the European Conference on Computer Vision (ECCV).2018:168-184.
[20]CHO W,CHOI S,PARK D K,et al.Image-to-image translation via group-wise deep whitening-and-coloring transformation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:10639-10647.
[21]MA Z,LI J,WANG N,et al.Semantic-related image style trans-fer with dual-consistency loss[J].Neurocomputing,2020,406:135-149.
[22]CHOI Y,CHOI M,KIM M,et al.Stargan:Unified generativeadversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8789-8797.
[23]SANAKOYEU A,KOTOVENKO D,LANG S,et al.A style-aware content loss for real-time hd style transfer[C]//Procee-dings of the European Conference on Computer Vision (ECCV).2018:698-714.
[24]WU H M,LIU Q R,WANG Y H.Face image translation based on generative adversarial networks[J].Journal of Tianjin University:Science and Technology,2019,52(3):306-314.
[25]PENG Y F,WANG K X,MEI J Y,et al.Image style migration based on cycle generative adversarial networks[J].Computer Engineering & Science,2020,42(4):699-706.
[26]BAO J,CHEN D,WEN F,et al.CVAE-GAN:fine-grainedimage generation through asymmetric training[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2745-2754.
[27]WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:3-19.
[28]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[29]ULYANOV D,VEDALDI A,LEMPITSKY V.Instance nor-malization:The missing ingredient for fast stylization[J].arXiv:1607.08022,2016.
[30]BA J L,KIROS J R,HINTON G E.Layer normalization[J].arXiv:1607.06450,2016.
[31]JOLICOEUR-MARTINEAU A.The relativistic discriminator:a key element missing from standard GAN[J].arXiv:1807.00734,2018.
[32]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
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