Using Fully Connected and Convolutional Net for GAN-Based Face Swapping (2024)

Lin, B. S., Hsu, D. W., Shen, C. H. (2020). Using Fully Connected and Convolutional Net for GAN-Based Face Swapping. 於 X.-T. Tran, & D.-H. Bui (編輯), Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 (頁 185-188). 文章 9301665 (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCCAS50809.2020.9301665

@inproceedings{9bf63f8fc7f54180b5915d28533a5a0a,

title = "Using Fully Connected and Convolutional Net for GAN-Based Face Swapping",

abstract = "The lifelike results of using face swapping have contributed greatly to the research in computer vision. In this work, we extend the architecture of faceswap-GAN in order to obtain more natural results compared to the original framework. In the original architecture, the self-attention module usually converts the facial features from a source face to the target face with artificial distortion around the facial features. We use a structure of fully connected convolutional layers as a discriminator to approach the problem. The outcome can be smoother and more natural perceptually compared to the results using the original faceswap-GAN.",

keywords = "Deepfake, fully-connected and convolutional network, Generative adversarial network (GAN)",

author = "Lin, {Bo Shue} and Hsu, {Ding Wen} and Shen, {Chin Han} and Hsu-Feng Hsiao",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 ; Conference date: 08-12-2020 Through 10-12-2020",

year = "2020",

month = dec,

day = "8",

doi = "10.1109/APCCAS50809.2020.9301665",

language = "American English",

series = "Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "185--188",

editor = "Xuan-Tu Tran and Duy-Hieu Bui",

booktitle = "Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020",

address = "United States",

}

Using Fully Connected and Convolutional Net for GAN-Based Face Swapping. / Lin, Bo Shue; Hsu, Ding Wen; Shen, Chin Han 等.
Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020. 編輯 / Xuan-Tu Tran; Duy-Hieu Bui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 185-188 9301665 (Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - Using Fully Connected and Convolutional Net for GAN-Based Face Swapping

AU - Lin, Bo Shue

AU - Hsu, Ding Wen

AU - Shen, Chin Han

AU - Hsiao, Hsu-Feng

N1 - Publisher Copyright:© 2020 IEEE.Copyright:Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/12/8

Y1 - 2020/12/8

N2 - The lifelike results of using face swapping have contributed greatly to the research in computer vision. In this work, we extend the architecture of faceswap-GAN in order to obtain more natural results compared to the original framework. In the original architecture, the self-attention module usually converts the facial features from a source face to the target face with artificial distortion around the facial features. We use a structure of fully connected convolutional layers as a discriminator to approach the problem. The outcome can be smoother and more natural perceptually compared to the results using the original faceswap-GAN.

AB - The lifelike results of using face swapping have contributed greatly to the research in computer vision. In this work, we extend the architecture of faceswap-GAN in order to obtain more natural results compared to the original framework. In the original architecture, the self-attention module usually converts the facial features from a source face to the target face with artificial distortion around the facial features. We use a structure of fully connected convolutional layers as a discriminator to approach the problem. The outcome can be smoother and more natural perceptually compared to the results using the original faceswap-GAN.

KW - Deepfake

KW - fully-connected and convolutional network

KW - Generative adversarial network (GAN)

UR - http://www.scopus.com/inward/record.url?scp=85099568212&partnerID=8YFLogxK

U2 - 10.1109/APCCAS50809.2020.9301665

DO - 10.1109/APCCAS50809.2020.9301665

M3 - Conference contribution

AN - SCOPUS:85099568212

T3 - Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

SP - 185

EP - 188

BT - Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

A2 - Tran, Xuan-Tu

A2 - Bui, Duy-Hieu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

Y2 - 8 December 2020 through 10 December 2020

ER -