Secure Facial Verification: A hybrid model for detecting Spoof Attacks with ResNet50-DenseNet121 | ||||
| International Journal of Telecommunications | ||||
| Volume 04, Issue 02, July 2024 PDF (1.87 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijt.2024.293947.1055 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Aya ElSayed  1; Noha A. Hikal2; Nehal A. Sakr3; Ali E Takieldeen 4
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| 1Faculity of Artificial Intelligence , Delta University for Science and Technology , Gamsa, Egypt | ||||
| 2Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt | ||||
| 3Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt, | ||||
| 4IEEE Senior Member, Faculty of Artificial Intelligence, Delta University for Science and Technology | ||||
| Abstract | ||||
| The present study introduces a novel hybrid deep learning model, leveraging the synergies inherent in the amalgamation of ResNet50 and DenseNet121 architectures. This fusion aims to effectively tackle the formidable task of detecting spoof attacks. Spoof attacks pose a significant threat to digital systems and networks, where adversaries attempt to deceive systems by impersonating legitimate users or sources. The proposed hybrid model aims to enhance detection accuracy and robustness against various spoof attacks by leveraging the complementary features of ResNet50 and DenseNet121. Integrating these architectures creates a unified framework that effectively captures local and global input data features, enabling more comprehensive detection capabilities. The problem of detecting spoofing attacks is stated as a classification task, and we train the hybrid model using large-scale datasets comprising fake and real data samples. The experimental results illustrate the superior performance of the proposed hybrid model in comparison to individual SVM, KNN, CNN, and RNN models, highlighting its efficacy in mitigating the risks associated with spoof attacks in digital systems and networks. | ||||
| Keywords | ||||
| Hybrid Model; Deep learning techniques; Spoof attacks; ResNet50; Densenet121 | ||||
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