Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221000241-10.doi: 10.11896/jsjkx.221000241

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Controlled Facial Gender Forgery Combining Wavelet Transform High Frequency Information

CHEN Wanze, CHEN Jiazhen, HUANG Liqing, YE Feng, HUANG Tianqiang, LUO Haifeng   

  1. College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China
  • Published:2023-11-09
  • About author:CHEN Wanze,born in 1995,postgra-duate.His main research interests include facial image synthesis,facial attribute operation,etc.
    CHEN Jiazhen,born in 1971,associate professor.Her main research interest is information security.
  • Supported by:
    National Natural Science Foundation of China(62072106),Natural Science Foundation of Fujian Province,China(2020J01168,2022J01190) and Scientific Research Fundation of the Education Department of Fujian Province,China(JAT210053).

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Abstract: Image-to-image translation(I2I) technology based on generative adversarial networks has made a series of breakthroughs in various fields,and is widely used in image synthesis,image coloring,and image super-resolution,especially in face attribute manipulation.To solve the issue of disparity in the performance of generated images in different translation directions due to model architecture and data imbalance,an high-frequency injection GAN(HFIGAN) model is proposed to achieve controlled facial gender forgery for transmitting high-frequency information.Firstly,in the wavelet module for transmitting high-frequency information,the features in the coding stage are decomposed at the feature level by discrete wavelet transform,and the obtained high-frequency information is injected reciprocally in the decoding stage,so that the information composition between the source and target domains is always in a more desirable ratio.Second,images’ dynamic consistency loss addresses the inconsistent translation difficulty in different directions for multi-domain conversion tasks in I2I.By redesigning the loss function,we scale the loss of difficult and easy samples,improve the feedback of difficult samples to the model,and make the model focus more on training difficult samples to improve performance.Finally,the diversity regular term based on style features is proposed to add the distance metric of style vectors in different spaces to the traditional diversity loss for supervision,which enables the model to maintain the diversity of generated images while improving the quality of image generation.Experiments on CelebA-HQ dataset and FFHQ dataset verify the effectiveness of the proposed method.The generalization of the loss function is verified in the mainstream I2I model combined with the proposed loss in this paper.Experimental results show that HFIGAN has better performance in facial gender falsification compared with previous advanced methods,and the proposed loss function has some generality.

Key words: Image generation, Generative adversarial network, Image-to-Image translation, Facial attribute manipulation, Focal loss

CLC Number: 

  • TP391
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