# A Novel Deep Learning Approach for Deepfake Image Detection
## 6 Novel Proposed Approach  
A novel DFP approach is proposed based on a hybrid of VGG16 and convolutional neural network architecture. For the first time, our research study uses a hybrid of transfer learning and deep learning-based neural network architecture for deepfake detection. The VGG16 and convolutional neural network layers were combined to make the proposed model architecture. Convolutional neural networks are the family of artificial neural networks primarily used in image recognition applications. The convolutional neural networks are specifically designed to process pixel data. The image data is represented in \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sr No.** & **Layers** & **Unit** & **Activation Function** & **Output Shape** & **Parameters** \\ \hline
1 & The Input Sequential layers. & None & None & (100, 256, 256, 3) & 0 \\ \hline
2 & The Mobile Net layers. & 1024 & None & (None, 8, 8, 1024) & 3,228,864 \\ \hline
3 & The dropout layers. & 0.2 & None & (100, 8, 1024) & 0 \\ \hline
4 & The Flatten layers. & None & None & (100, 65,536) & 0 \\ \hline
5 & The Dense layers. & 64 & RELU & (100, 64) & 4,194,368 \\ \hline
6 & The Output layers. & 1 & SIGMOID & (100, 1) & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Mobile Net model configuration parameters and layer architectural analysis. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Sr No.** & **Layers** & **Unit** & **Activation Function** & **Output Shape** & **Parameters** \\ \hline
1 & The Input Sequential layers. & None & None & (100, 256, 256, 3) & 0 \\ \hline
2 & The VGG16 layers. & 512 & None & (None, 8, 512) & 14,714,688 \\ \hline
3 & The Dropout layers. & 0.2 & None & (100, 8, 8, 512) & 0 \\ \hline
4 & The Flatten layers. & None & None & (100, 32,768) & 0 \\ \hline
5 & The Dense layers. & 64 & RELU & (100, 64) & 2,097,216 \\ \hline
6 & The Output layers. & 1 & SIGMOID & (100, 1) & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The VGG16 model configuration parameters and layer architectural analysis. convolutional neural networks using multidimensional array forms. The primary aim of the artificial neural network is to learn patterns from historical data and make predictions for unseen data. The proposed model layer's architecture and configuration parameters are analyzed. The configuration parameters of the novel proposed approach are examined in Table 6. The configuration parameters described the units and parameters used during the proposed model building. The architecture analysis is visualized in Figure 3. The architectural analysis demonstrates the deepfake detection image data flow from input to prediction layers using the proposed technique. The hybrid layers of VGG16 and convolutional neural networks are involved in creating the architecture. The pooling, dropout, flatten, and fully-connected layers were combined to build the novel proposed model architecture. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sr No.** & **Layers** & **Unit** & **Activation Function** & **Output Shape** & **Parameters** \\ \hline
1 & The Sequential Input layers. & None & None & (100, 256, 256, 3) & 0 \\ \hline
2 & The VGG16 Network layers. & 512 & None & (None, 8, 8, 512) & 14,714,688 \\ \hline
3 & The Convolutional Network layers. & 1025 & RELU & (100, 6, 6, 1025) & 4,724,225 \\ \hline
3 & The max pooling layers. & 2 \(\times\) 2 & None & (100, 3, 3, 1025) & 0 \\ \hline
4 & The Dropout layers. & 0.02 & None & (100, 3, 3, 1025) & 0 \\ \hline
5 & The Flatten layers. & None & None & (100, 9225) & 0 \\ \hline
6 & The Dense layers. & 1025 & RELU & (100, 1025) & 9,456,650 \\ \hline
7 & The Dense layers. & 1 & SIGMOID & (100, 1) & 1026 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The novel proposed DFP model configuration parameters analysis. Figure 3: The novel proposed DFP model architectural analysis. The novel proposed DFP model has many advantages compared to state-of-the-art models. The proposed DFP network architecture is fully optimized.