# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Performance Evaluation of Baseline Models  
To establish a strong comparative foundation for evaluating the proposed hybrid architecture, three well-established deep learning models--ResNet-50, VGGNet, and DenseNet--were re-implemented and evaluated using the CIFAKE dataset. This dataset, comprising a balanced set of real and AI-generated images, served as a reliable benchmark to assess each model's capability in accurately distinguishing between synthetic and authentic visual content. Each model was trained under identical experimental conditions using 80% of the dataset for training and 20% for testing. This uniform configuration ensured a fair and consistent evaluation across all architectures. The SE-ResNet50 model emerged as the best-performing architecture among all evaluated models, achieving a test accuracy of 96.12%, precision of 97.04%, recall of 88.94%, F1-score of 92.82%, and a ROC-AUC score of 0.9862. This superior performance underscores the impact of integrating Squeeze-and-Excitation (SE) blocks into the ResNet-50 backbone. By recalibrating feature channels dynamically, the SE mechanism allows the network to focus more effectively on subtle and discriminative cues typically introduced by generative models, such as minor texture inconsistencies or unnatural patterns, thereby enhancing the model's ability to distinguish between real and synthetic images with greater precision. VGGNet, although architecturally simpler with its fixed-size (\(3\times 3\)) convolutional layers, achieved a test accuracy of 94.06%. Its deep and sequential design contributes to strong generalization but comes at the cost of higher computational demand and slower training. DenseNet, with its densely connected layers that promote efficient gradient flow and feature reuse, slightly outperformed VGGNet with a test accuracy of 95.98%. However, it lagged behind SE-ResNet50 in precision, recall, and F1-score, highlighting its relative weakness in maintaining classification balance between real and fake image categories. Overall, while both VGGNet and DenseNet performed competitively, SE-ResNet50 clearly outperformed them across most key metrics, demonstrating not only high classification accuracy but also strong generalization, low false-positive rates, and improved focus on semantically relevant features. These characteristics make it especially suitable for high-stakes applications such as digital content authentication, where both performance and interpretability are critical. Table 2 summarizes the performance metrics of baseline models evaluated on the CIFAKE dataset. Results indicate that the ResNet-50 model, enhanced with Squeeze-and-Excitation (SE) attention blocks, achieved the best performance among all the evaluated models. With an accuracy of 96.12%, precision of 97.04%, recall of 88.94%, and an F1-score of 92.82%, SE-ResNet50 outperformed both DenseNet and VGGNet across all major classification metrics. Its high ROC-AUC score of 0.9862 further confirms its strong discriminative capability between real and AI-generated images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **ROC-AUC** \\ \hline ResNet-50 & 96.12\% \(\pm\) 0.25\% & 97.04\% \(\pm\) 0.50\% & 88.94\% \(\pm\) 0.50\% & 92.82\% \(\pm\) 0.50\% & 0.9862 \\ VGGNet & 94.06\% \(\pm\) 0.42\% & 96.60\% \(\pm\) 0.42\% & 85.43\% \(\pm\) 0.42\% & 90.04\% \(\pm\) 0.42\% & 0.98 \\ DenseNet & 95.98\% \(\pm\) 0.26\% & 96.25\% \(\pm\) 0.26\% & 87.80\% \(\pm\) 0.26\% & 91.07\% \(\pm\) 0.27\% & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of baseline models. This superior performance can be attributed to the SE blocks, which dynamically recalibrate the importance of feature channels. By emphasizing informative features and suppressing less useful ones, the model becomes more sensitive to subtle visual discrepancies, such as texture artifacts or unnatural transitions, commonly found in synthetic images.