WildDeepfake: a challenging real-world dataset for deepfake detection

Zi, Bojia, Chang, Minghao, Chen, Jingjing, Ma, Xingjun and Jiang, Yu-Gang 2020, WildDeepfake: a challenging real-world dataset for deepfake detection, in MM 2020 : Proceedings of the 28th ACM International Conference on Multimedia, Association for Computing Machinery, New York, N.Y., pp. 2382-2390, doi: 10.1145/3394171.3413769.

Attached Files
Name Description MIMEType Size Downloads

Title WildDeepfake: a challenging real-world dataset for deepfake detection
Author(s) Zi, Bojia
Chang, Minghao
Chen, Jingjing
Ma, XingjunORCID iD for Ma, Xingjun orcid.org/0000-0003-2099-4973
Jiang, Yu-Gang
Conference name Association for Computing Machinery. Conference (28th : 2020 : Seattle, Washington)
Conference location Seattle, Washington
Conference dates 2020/10/12 - 2020/10/16
Title of proceedings MM 2020 : Proceedings of the 28th ACM International Conference on Multimedia
Editor(s) Chen, CW
Cucchiara, R
Hua, X-S
Qi, G-J
Ricci, E
Zhang, Z
Zimmermann, R
Atrey, PK
Li, Z
Publication date 2020
Series Association for Computing Machinery Conference
Start page 2382
End page 2390
Total pages 9
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) Datasets
deep learning
deepfake detection
ISBN 9781450379885
Language eng
DOI 10.1145/3394171.3413769
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152261

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 14 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Wed, 09 Jun 2021, 12:02:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.