# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 4 Experiment and Results
### Results and Comparison
While DenseNet has a maximum distance of positive pairs of 0.5223 and validation of a maximum distance of positive pairs of 0.7032. On the other hand, ResNet50 has a maximum distance of positive pairs of 0.5827 and validation of a maximum distance of positive pairs of 0.8199. These experimental results indicate that the EfficientNet-B0 model has a better representation of the data, which could be determined by the compound scaling method utilized in EfficientNet-B0. These results also mean that EfficientNet-B0 can learn more informative and selective features from the input data, which helps it to detect plagiarized images more precisely. However, the lowest validation maximum distance of positive pairs was not achieved by EfficientNet-B0, though DenseNet obtained it with 0.7032 among compared models, which indicates that DenseNet generalizes well to validation data and is better at detecting plagiarized images on the validation sets. The dense connections in the DenseNet architecture allow the model to learn more robust features that generalize well to the validation data [48], which may enable it to identify plagiarized images more accurately on the validation data. From Table 3, we can see that the EfficientNet-B0-based DNN has achieved the highest maximum distance of negative pairs, with a training result of 1.133 and a maximum validation distance of negative pairs of 1.0150. MobileNetV2 has a maximum distance of negative pairs of 0.9634, DenseNet has a maximum distance of negative pairs of 1.090 and a maximum validation distance of negative pairs of 0.9342, and ResNet50 has a maximum distance of negative pairs of 1.074 and a maximum validation distance of negative pairs of 0.9817. These results indicate that the EfficientNet-B0 model can learn more robust features that can capture subtle differences between the original and plagiarized images, resulting in a higher maximum distance of negative pairs. Figure 11 compares all models in maximum negative pairs. Figure 10: The comparison of the maximum distance of positive pairs among ResNet50, DenseNet, MobileNetV2, and EfficientNet-B0. From Table 4, we can see that EfficientNet-B0 has the highest accuracy on the training set among MobileNetV2, DenseNet, and ResNet50 with threshold scores of 0.55, 0.65, and 0.75. This outcome implies that EfficientNet-B0 is better at generalizing to the validation data and identifying plagiarized images on the validation set when compared to the other models. The EfficientNet-B0 model can learn more robust features that can capture slight differences between the original and plagiarized images, resulting in higher accuracy on the validation set. Figures 12-14 show the results of accuracy and validation accuracy from the compared models in this study with various threshold scores of 0.55, 0.65, and 0.75, respectively. In addition, Table 4 shows that DenseNet has the highest validation accuracy among the other models (EfficientNet-B0, MobileNetV2, and ResNet50) with threshold scores of 0.55, 0.65, and 0.75. This result is presumably due to DenseNet's architecture using dense connections between layers and a feed-forward technique where each layer is connected to all the other layers. The DenseNet method allows the gradients to flow directly from any layer to any other layer, enabling the model to learn more robust features that can capture slight differences between the original and plagiarized images [49]. The dense connections in DenseNet architecture also reduce the risk of overfitting, allowing the model to generalize better to the validation data [50]. Figure 11: The comparison of the minimum distance of negative pairs among ResNet50, DenseNet, MobileNetV2, and EfficientNet-B0.