# Sensitive Information Detection Based on Deep Learning Models
## 4 Experiments
### Experimental Results and Analysis
#### 4.4.6 Visualization Analysis  
To visually demonstrate the predictive capabilities of our model for sensitive content, we plotted the confusion matrices of the improved model and YOLOv8n, as shown in Figure 8. The horizontal axis of the confusion matrix represents the true classes, while the vertical axis represents the predicted classes by the model. The diagonal values indicate the proportion of correct predictions for each class, while the off-diagonal values represent the proportion of misclassifications. From Figure 8, it can be observed that compared to the confusion matrix of YOLOv8n, most of the values on the diagonal of the confusion matrix of the improved model have increased. This indicates that the improved model has significantly improved performance across multiple categories, especially in recognizing some challenging sensitive categories. For example, in the 'EXPOSED_GENITALIA_F' category, the accuracy has increased from 0.60 to 0.67. As adjustments were made to the feature extraction mechanism during model improvement to better capture features related to 'EXPOSED_GENITALIA_F' and 'EXPOSED_GENITALIA_M', this resulted in a slight decrease in accuracy for the 'EXPOSED_BREAST_F' category. We will continue to adjust the model to balance the performance of all categories in subsequent iterations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP0.5\%** & **Precision\%** & **Recall\%** & **Params (M)** & **GFLOPs** \\ \hline YOLOv3-tiny & 70.2 & 72.8 & 62.1 & 12.13 & 18.9 \\ YOLOv5n & 72.0 & 79.5 & 61.7 & 2.50 & 7.1 \\ RT-DETR & 77.9 & 83.3 & 68.9 & 19.88 & 57.0 \\ Gold-YOLO & 74.9 & 79.7 & 64.8 & 6.01 & 11.9 \\ YOLOv9-c & 78.1 & 81.6 & 70.1 & 50.71 & 236.7 \\ YOLOv8n & 73.8 & 78.3 & 64.3 & 3.00 & 8.1 \\ Ours & 79.7 & 83.4 & 70.7 & 6.61 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different models in detection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP0.5\%** & **Precision\%** & **Recall\%** & **Params (M)** & **GFLOPs** \\ \hline YOLOv8n & 73.8 & 75.7 & 67.0 & 3.00 & 8.1 \\ Ours & 76.5 & 77.8 & 69.1 & 6.61 & 10.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different models in detection on the Pascal VOC dataset.  
To showcase the precision of model predictions for positive classes at various recall levels, we generated PR (Precision-Recall) curves for both the improved model and YOLOv8n, as depicted in Figure 9. Upon comparison, it is evident that the improved model  
Figure 8: Confusion matrix comparative effectiveness diagram.  
exhibits significantly enhanced accuracy across most categories, particularly notable in the 'EXPOSED_GENITALIA_F' category where accuracy has surged from 0.591 to 0.798. Moreover, the average precision ([EMAIL_ADDRESS]) for all categories in the improved model has escalated from 0.738 to 0.797, further confirming the superior overall performance of our model over YOLOv8n. Despite these advancements, we acknowledge there is still room for improvement in the 'EXPOSED_GENITALIA_M' category.  
Figure 9: Comparison effect diagram of PR curves.