# Deep Learning-Based Digital Image Forgery Detection System
## 5 Experimental Results and Discussion
### Results Discussion and Comparison  
We compared the convergence performance of the proposed deep learning-based approach with and without transfer learning. Figure 10 shows the comparison of results of the CASIA_v1 and CASIA_v2 datasets with transfer learning. The blue color represents the accuracy per epoch for the CASIA_v1 dataset, whereas the orange color represents the accuracy for the CASIA_v2 dataset. A total of 25 epochs were executed to check the effectiveness of our proposed technique. After the execution of the first epoch, we saw a strong positive trend for both datasets. We achieved an accuracy of 99.3% with CASIA_v2, and 81% with the CASIA_v1 dataset. Since CASIA_v2 is considered to be a more comprehensive and benchmark dataset, the experimental results obtained with the CASIA_v2 dataset are good compared to those from the CASIA_v1 dataset. The equation for calculating the accuracy is given in Equation (2). \[Accuracy=\frac{TP+TN}{Total\ Samples} \tag{2}\]  
The comparison of the results of our proposed architecture without using transfer learning is shown in Figure 11. This approach had adverse effects on our proposed system architecture, as the model learned from scratch with randomly initialized weights. Using random initialization for weights tends to increase the complexity, cost, and training time. The random initialization is non-meaningful, so the model had to do a lot of work to update the weights. On the other hand, the use of transfer learning enabled us to use pre-trained weights that are meaningful. The model did not have to be trained from scratch, thus decreasing the training time, cost, complexity, and increasing the accuracy. In our proposed  
Figure 10: Testing process visualization with transfer learning. \begin{table}
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**Name** & **Details** \\ \hline Operating system & Windows 10 \\ Programming language & Python \\ Model & Asus ROG 702 VM \\ RAM & 32 GB \\ Cores & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System specification details. deep learning-based architecture, we used the pre-trained YOLO CNN weights. You Only Look Once (YOLO) is a real-time, state-of-the-art object detection system that is trained on thousands of different objects. Figures 10 and 11 explain the performance evaluation of the proposed technique with and without transfer learning, respectively. We achieved an accuracy of 80% without the use of transfer learning in the CASIA_v2 dataset, while we obtained an accuracy of 69.3% for the CASIA_v1 dataset. Figures 12 and 13 present the comparison of the losses between transfer learning and without transfer learning among the two datasets. An increase in loss is seen when we did not use the pre-trained weights of the YOLO CNN model. The loss was slightly higher in the CASIA_v1 dataset compared to CASIA_v2. Furthermore, we compared our proposed method with existing architectures, which can be seen in Table 4, which presents the type of tampering targeted, along with the methodology used to detect the tampering. We also list the advantages and disadvantages of the respective architectures and their obtained accuracies. Figure 11: Testing process visualization without transfer learning. Figure 12: Comparison between losses on CASIA_v2 dataset. Cross-validation is a technique used to divide the data into a given number of sets and train the model on each set. Table 4 shows the evaluations and performance of five folds. The average accuracy obtained was 99.33% and the average loss was 0.076. According to the comparative analysis with the methodologies mentioned in Table 5, the proposed architecture performed better compared to the traditional tampering detection techniques.