VIOLA jones algorithm with capsule graph network for deepfake detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020454" target="_blank" >RIV/62690094:18470/23:50020454 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-1313/" target="_blank" >https://peerj.com/articles/cs-1313/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.1313" target="_blank" >10.7717/peerj-cs.1313</a>
Alternative languages
Result language
angličtina
Original language name
VIOLA jones algorithm with capsule graph network for deepfake detection
Original language description
DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones's (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ Computer Science
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
9
Issue of the periodical within the volume
April
Country of publishing house
GB - UNITED KINGDOM
Number of pages
25
Pages from-to
"Article Number: e1313"
UT code for WoS article
000996343300001
EID of the result in the Scopus database
2-s2.0-85159173260