# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Performance of Proposed Hybrid Model (SE-ResNet-50)  
To overcome the limitations identified in the baseline models, particularly in handling nuanced visual cues and computational trade-offs, a hybrid model--SE-ResNet50--has been proposed and rigorously evaluated on the same CIFAKE dataset. This architecture augments the traditional ResNet-50 framework with Squeeze-and-Excitation (SE) blocks, a channel-wise attention mechanism designed to dynamically recalibrate the importance of feature maps. The goal was to enable the network to focus more selectively on image regions that contain subtle generative artifacts often missed by traditional convolutional networks.  
The SE-ResNet-50 model demonstrated the best classification performance among all tested models, achieving an accuracy of 96.12%, a precision of 97.04%, a recall of 88.94%, and an F1-score of 92.82%. Additionally, the model attained a high ROC-AUC score of 0.9862, indicating excellent discrimination capability between real and synthetic images across classification thresholds. These results confirm SE-ResNet50 as the most balanced and effective model for the detection task.  
Table 6 presents a detailed comparison of core evaluation metrics.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **SE-ResNet50 (\%)** \\ \hline Accuracy & 96.12 \(\pm\) 0.25 \\ Precision & 97.04 \(\pm\) 0.50 \\ Recall & 88.94 \(\pm\) 0.50 \\ F1-Score & 92.82 \(\pm\) 0.50 \\ ROC-AUC & 0.9862 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics of SE-ResNet50 on the CIFAKE dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **DenseNet Correct** & **DenseNet Wrong** \\ \hline SE-ResNet50 Correct & 8780 & 918 \\ SE-ResNet50 Wrong & 189 & 113 \\ \hline \hline \end{tabular}
\end{table}
Table 5: McNemar contingency table (SE-ResNet50 vs. DenseNet).  
The superior performance of the SE-ResNet50 model--achieving 96.12% accuracy and a ROC-AUC score of 0.9862--is primarily attributed to the incorporation of SE blocks into the ResNet-50 architecture. These blocks perform dynamic, channel-wise recalibration of feature maps by leveraging global context through the squeeze operation and reweighting informative features via excitation. As a result, the model is able to highlight fine-grained patterns and subtle visual anomalies--such as edge inconsistencies or low-level artifacts--that are often overlooked by conventional convolutional layers. This architectural enhancement improves class separability and reduces misclassification, particularly in challenging or visually ambiguous cases, contributing directly to the model's high classification fidelity.  
The precision score of 97.04% reflects the model's ability to reduce false positives, while the recall score of 88.94% demonstrates its effectiveness in detecting AI-generated images. The F1-score of 92.82% highlights its overall robustness in balancing sensitivity and specificity. With the highest test performance across all evaluated models, SE-ResNet-50 emerges as the optimal choice for real-world applications requiring both accuracy and efficiency.