Deep fake image detection based on deep learning using a hybrid CNN-LSTM with machine learning architectures as classifier
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
One of the most important and difficult subjects in social communication is detecting deepfake images and videos. Deepfake techniques have developed widely, making this technology quite available and proficient enough so that there is worry about its bad application. Considering this issue, discovering fake faces is very important for ensuring security and preventing sociopolitical issues on a private and general level. Deep learning provides higher performance than typical image processing approaches when it comes to deepfake detection. This work presents construction of an artificial intelligence system, which is capable of detecting deepfake from more than one dataset. This study proposes neural network models based on deep learning using random forest (RF) and support vector machines (SVM) as classifier for deepfake detection. The use of two classifiers (RF) and (SVM) and their combination with a convolutional neural network is the first study of its kind in the field of deepfake detection in images from three open-source datasets (FaceForensics++, FaceAntiSpoofing, and iFakeFaceDB). This proposed method shows an accuracy of 96%, 87% and 52% in iFakeFaceDB, CelebA-Spoof, FaceForensics++ and respectively.
Açıklama
Anahtar Kelimeler
Convolutional neural network, DeepFake detection, Machine learning
Kaynak
HORA 2024 - 6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Al-Dulaimi, O. A. H. H., Kurnaz, S. (2024). Deep fake image detection based on deep learning using a hybrid CNN-LSTM with machine learning architectures as classifier. HORA 2024 - 6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings. 10.1109/HORA61326.2024.10550728
