Deepfake detection using rationale-augmented convolutional neural network
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Deepfake network is a prominent topic of research as an application to various systems about security measures. Although there have been many recent advancements in facial reconstruction, the greatest challenge to overcome has been the means of finding an efficient and quick way to compute facial similarities or matches. This work is created utilizing the rationale-augmented convolutional neural network (CNN) on MATLAB R2019a platform using the Kaggle DeepFake Video dataset with an accuracy of 95.77%. Hence, real-time deepfake facial reconstruction for security purposes is difficult to complete concerning limited hardware and efficiency. This research paper looks into rational augmented CNN state-of-the-art technology utilized for deepfake facial reconstruction via hardware such as webcams and security cameras in real time. Additionally, discuss a history of face reconstruction and provide an overview of how it is accomplished.
Açıklama
Anahtar Kelimeler
Deepfake, Video, Detection, Segmentation, Facial alignment, Deep learning, Reconstruction
Kaynak
Applied Nanoscience
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
