Analyzing Fairness in Deepfake Detection With Massively Annotated Databases-Reference-Cited by-同舟云学术

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Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

Published:2024-03 Issue:1 Volume:5 Page:93-106
ISSN:2637-6415
Container-title:IEEE Transactions on Technology and Society
language:
Short-container-title:IEEE Trans. Technol. Soc.

Author:

Xu Ying¹^ORCID,Terhörst Philipp¹^ORCID,Pedersen Marius¹^ORCID,Raja Kiran¹^ORCID

Affiliation:

1. Institutt for datateknologi og informatikk, Norges teknisk-naturvitenskapelige universitet, Gjøvik, Norway

Funder

European Research Consortium for Informatics and Mathematics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8566059/10539297/10438899.pdf?arnumber=10438899

Reference61 articles.

1. Deepfakes and beyond: A Survey of face manipulation and fake detection

2. Generative adversarial nets;Goodfellow

3. Deep learning in neural networks: An overview

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning Self-distilled Features for Facial Deepfake Detection Using Visual Foundation Models: General Results and Demographic Analysis;Journal on Interactive Systems;2024-07-09

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