SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data-Reference-Cited by-同舟云学术

SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Joint Conference on Biometrics (IJCB)
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Author:

Huber Marco¹,Boutros Fadi¹,Luu Anh Thi¹,Raja Kiran²,Ramachandra Raghavendra²,Damer Naser¹,Neto Pedro C.³,Goncalves Tiago³,Sequeira Ana F.³,Cardoso Jaime S.³,Tremoco Joao⁴,Lourenco Miguel⁴,Serra Sergio⁵,Cermeno Eduardo⁵,Ivanovska Marija⁶,Batagelj Borut⁶,Kronovsek Andrej⁶,Peer Peter⁶,Struc Vitomir⁶

Affiliation:

1. Fraunhofer Institute for Computer Graphics Research IGD,Germany

2. Norwegian University of Science and Technology,Norway

3. INESC TEC,Portugal

4. Yoonik,Portugal

5. Vaelsys R&D,Spain

6. University of Ljubljana,Slovenia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10007927/10007928/10007950.pdf?arnumber=10007950

Reference56 articles.

1. Face Morphing Detection: An Approach Based on Image Degradation Analysis

2. One millisecond face alignment with an ensemble of regression trees

3. Analyzing and improving the image quality of style-gan;karras;2020 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2020,2020

4. A style-based gener-ator architecture for generative adversarial networks;karras;CVPR,2019

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