# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 5 Experimentation and Results
### Images' Utility Maintenance  
Aiming to improve the quality of generated images of the model, i.e., to obtain the minimum loss rate to image quality, we adjusted the key parameter \(\alpha\), which allocates the proportion of the quality loss, while keeping \(\beta\) fixed at 0.2, and checked the model effect of quality maintenance to each value of \(\alpha\), respectively. The downward trend of quality loss is presented in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **0.2** & **0.4** & **0.5** & **0.6** & **0.8** & **1** \\ \hline Ratio of protected images & 58.8\% & 79.2\% & 87.4\% & 92.0\% & 96.2\% & 98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ratio of the cumulative number of privacy-protected images in different models. Figure 6: Demonstration of de-identified images. Figure 7: The changing trend of expression loss rate. As the figure above illustrates, with a value of 0.4 of \(\alpha\), the model yielded the best output. After 50 epochs, the loss was reduced to approximately 0.23, which was the lowest among other types of \(\alpha\) percent. This achieved a semantic retention rate of 77%, demonstrating the efficacy of the proposed approach in preserving the semantics of images while de-identifying. In general, using an \(\alpha\) value of 0.4 for the output is more beneficial in terms of training iterations as compared to using a simple image fusion approach. This suggests that within the overall loss function, semantic loss plays a crucial role in maintaining the quality of the generated images. Our method contains expression loss, which plays an important role in preserving semantics, outperforming other face privacy protection models that do not take the quality of facial semantics (facial expression) into consideration such as AMT-GAN [8]. While AMT-GAN had a loss rate of 0.37, the proposed model only lost 0.23, demonstrating a better performance overall. The results are presented in Figure 8, further supporting the effectiveness of the FPSP model. We performed the following analyses under an \(\alpha\) value of 0.4. Let \(N_{unchanged}\) be the number of processed images with unchanged expression labels and \(N_{category}\) be the total number of images in the dataset. The loss rate of six expressions that underwent processing using different methods is computed using Equation (21). The results of this analysis are presented in Table 3, demonstrating their respective impacts on the loss rate of the expressions. \[Loss\ rate=\frac{N_{unchanged}}{N_{category}} \tag{21}\]  
Upon analysis of the results presented in Table 3, it becomes apparent that the proposed model was able to maintain the service quality of each expression to a certain degree, highlighting that our model is a practical approach that can be applied to the dataset for protecting privacy while preserving utility. The slightly elevated rate of the fear group and surprise group was due to the limited size of the sample. We computed the disparity between the values of the facial expression recognition outcomes for the original images and those processed by six different methods. Subsequently, we generated an error bar plot to represent the results, which is presented in Figure 9. As  
Figure 8: Representation of output images generated by the FPSP model and AMT-GAN. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Loss Rate** & **Angry** & **Fear** & **Happy** & **Sad** & **Surprise** & **Neutral** \\ \hline Our Model & 0.235 & 0.342 & 0.136 & 0.273 & 0.429 & 0.231 \\ Blindfold & 0.441 & 0.737 & 0.029 & 0.669 & 0.893 & 0.814 \\ Cartoon & 0.794 & 0.513 & 0.489 & 0.360 & 0.785 & 0.459 \\ Laplace & 0.911 & 0.895 & 0.665 & 0.756 & 0.964 & 0.702 \\ Mosaic & 0.294 & 0.302 & 0.299 & 0.361 & 0.321 & 0.169 \\ AMTGAN & 0.625 & 0.764 & 0.120 & 0.635 & 0.740 & 0.536 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Facial expression loss rate under different methods.