A hybrid CNN-LSTM approach for precision deepfake image detection based on transfer learning
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces for security and preventing socio-political issues. In the digital media era, deep learning outperforms traditional image processing methods in deepfake detection, underscoring its significance. This research introduces an innovative approach for detecting deepfake images by employing transfer learning in a hybrid architecture that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The hybrid CNN-LSTM model exhibits promise in combating deep fakes by merging the spatial awareness of CNNs with the temporal context understanding of LSTMs. Demonstrating effective performance on open-source datasets like “DFDC” and “Ciplab”, the proposed method achieves an impressive precision of 98.21%, indicating its capability to accurately identify deepfake images with a limited false-positive rate. The model’s error rate is 0.26%, emphasizing the challenges and intricacies inherent in deepfake detection tasks. These findings underscore the potential of hybrid deep learning techniques for addressing the urgent issue of deepfake image detection.
Açıklama
Anahtar Kelimeler
CNN, Deepfake, LSTM, ResNet, Transfer learning
Kaynak
Electronics (Switzerland)
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
13
Sayı
9
Künye
Al-Dulaimi, O. A. H. H., Kurnaz, S. (2024). A hybrid CNN-LSTM approach for precision deepfake image detection based on transfer learning. Electronics (Switzerland), 13(9). 10.3390/electronics13091662
