# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 4 Experiment and Results
### Results and Comparison  
In this study, we applied the EfficientNet-B0 with the Triplet Semi-Hard Loss model to detect plagiarism using the same dataset for three examples of threshold scores, which are 0.55, 0.65, and 0.75, to acquire the optimum score for examining the plagiarism of images. Overall, The proposed EfficientNet-B0-based DNN with Triplet Semi-Hard Loss retains the lowest loss and highest accuracy compared to ResNet50, DenseNet, and MobileNetV2 at various sampling threshold scores, which means that image plagiarism detection can be ensured. Figure 8: The triplet loss trains a neural network to increase the difference between the embeddings of different classes while minimizing the difference between the embeddings of the same class by selecting an anchor, a positive sample, and a negative sample. According to the experiments, EfficientNet-B0 with Triplet Semi-Hard Loss has reached the lowest loss, 0.1242, and lowest validation loss, 0.2808, compared to the other methods as elaborated in Table 2. Furthermore, as shown in Figure 9, the graph of EfficientNet-B0, represented by the red line, has the lowest loss, and the red dashed line depicts the lowest validation loss compared to the other three models. These results indicate the efficiency of the proposed approach in using computation resources. EfficientNet-B0 models use a compound scaling method that scales the dimensions of the network (depth, width, and resolution) in a computationally efficient manner, resulting in better performance with lesser resources [37]. Additionally, using a triplet semi-hard loss function can contribute to the modest loss since it can lower the training complexity by choosing negative pairs close to the anchor but adequately distant from it [45]. Additionally, Table 2 displays the loss and validation loss and the training time results for all four models. The MobileNetV2 model's training time was the shortest at 222.148 min, while the EfficientNet-B0 model's training time was the longest at 230.379 min. MobileNetV2 has a smaller structure with a size of 14 MB and a depth of 105, whereas EfficientNet-B0 has a structure size of 29 MB and a depth of 132 [46]. Therefore, based on model architecture, EfficientNet-B0 has a larger and more complex model than the other models, with more parameters and layers. This model can increase the training time as more computations are required to update the weights and biases of the model. Furthermore, the EfficientNet-B0 model is designed to handle larger images and batch sizes than the other models, which can raise the training time [47]. Figure 10 illustrates the EfficientNet-b0 model with the lowest maximum distance of positive pairs, which is represented by the red line, followed by DenseNet with the purple line, ResNet50 with the green line, then MobileNetV2 with the blue line, respectively. While the lowest validation maximum distance of positive pairs was achieved by DenseNet with \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Loss** & **Validation Loss** & **Training Times** \\ \hline ResNet50 & 0.1961 & 0.3507 & 228.677 \\ DenseNet & 0.2018 & 0.3346 & 223.992 \\ MobileNetV2 & 0.2533 & 0.4040 & 222.148 \\ EfficientNet-B0 & 0.1242 & 0.2808 & 230.379 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of loss and validation loss for all models. Figure 9: The comparison of loss and validation loss from ResNet50, DenseNet, MobileNetV2, and EfficientNet-B0. a purple dashed line, then followed by EfficientNet-B0 with a red dashed line, ResNet50 with a green dashed line, then MobileNetV2 with a blue dashed line subsequently. From Table 3, we can see the detailed results of the experiments that EfficientNet-B0 has the lowest maximum distance of positive pairs with 0.4526 and validation maximum distance of positive pairs of 0.8059 when compared to other MobileNetV2, DenseNet, and ResNet50. MobileNetV2 has a maximum distance of positive pairs of 0.5889 and validation of the maximum distance of positive pairs of 0.8457.