Scopus
YÖKSİS Eşleşti
Image forgery detection by combining Visual Transformer with Variational Autoencoder Network
Applied Soft Computing · Kasım 2024
YÖKSİS Kayıtları
Image forgery detection by combining Visual Transformer with Variational Autoencoder Network
Applied Soft Computing · 2024 SCI-Expanded
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Makale Bilgileri
DergiApplied Soft Computing
Yayın TarihiKasım 2024
Cilt / Sayfa165
Scopus ID2-s2.0-85200601395
Özet
Recently, the applications and artificial intelligences used for image manipulation have become quite successful. In this case, the manipulation of personal data can lead to problems of insurmountable magnitude. Such problems not only put personal data at risk, but also lead us to unethical practices, with potentially irreversible negative consequences. For this reason, the reliability of image or video data is highly questionable. To solve this challenging problem, we introduce a Visual Transformer based Visual Transformer with Variational Autoencoder Network (ViT-VAE Net) model. The model includes Visual Transformer, one of the state-of-the-art architectures. In addition to this architecture, a Variational Auto Encoder structure is also included. is much more effective than models developed with the classical Convolutional Neural Network (CNN). Unlike models developed with CNN, it can perform operations on images of any size without being bound by a standard image resolution. In addition, thanks to the self-attention mechanism in the Visual Transformer architecture, manipulations on the image are caught more easily than CNN. The ViT-VAE Net model was trained with a large dataset and tested with 4 different datasets. With a success rate of 67 % on the training dataset, the model provided promising results. Very high rates were also obtained with the test datasets.
Yazarlar (2)
1
Ilker Galip Atak
2
Ali Yasar
Anahtar Kelimeler
Image forgery detection
Self-attention
Visual Transformer
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey