# An Ensemble Transfer Learning Model for Detecting Stego Images
## 4 Results and Analysis
This was likely because higher payload levels introduce more errors into the steganography process, making them harder to detect. Overall, the results suggested that ResNet-50 and Inception are the most effective methods for error detection, with consistently higher EDRs across all payload levels. However, VGG16 Stego was also effective at lower payload levels (e.g., 0.2 bpp) while ANN had high EDRs across all payload levels. Table 2 shows the accuracy, precision, recall, and F1 score of each model. Based on the results, the CNN model achieved a better result than other methods did. Based on the evaluation metrics, the CNN model achieved the best result compared to the other models. To separate the valuable characteristics from the noise in the raw signal, a CNN model was employed. By using the provided CNN, we were able to achieve optimal results for both accuracy and speed. It followed that the chosen CNN characteristics would \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Payload bpp = 0.1** & **Payload bpp = 0.2** & **Payload bpp = 0.3** & **Payload bpp = 0.4** \\ \hline
**CNN** & 0.3624 & 0.3324 & 0.2501 & 0.1425 \\
**AlexNet** & 0.3925 & 0.3525 & 0.2845 & 0.2445 \\
**ResNet-50** & 0.3984 & 0.3484 & 0.2925 & 0.2531 \\
**Inception** & 0.3934 & 0.3434 & 0.2754 & 0.2465 \\
**VGG16 Stego [11]** & - & 0.3428 & - & 0.2354 \\
**ANN [31]** & 0.3724 & 0.3547 & 0.3375 & 0.3245 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error detection evaluation metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
**CNN** & 86\% & 84\% & 87\% & 84\% \\
**AlexNet** & 76\% & 75\% & 79\% & 79\% \\
**ResNet-50** & 75\% & 74\% & 78\% & 78\% \\
**Inception** & 76\% & 73\% & 72\% & 74\% \\
**VGG16 Stego [11]** & 82 \% & - & - & - \\
**ANN [31]** & 75\% & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics for the four proposed models. serve as good features for a method, which was in line with the current results. It was not necessary to complete a denoising step before implementing the proposed method. Table 2 shows the evaluation metrics for the four proposed models (CNN, AlexNet, ResNet-50, and Inception) for image steganalysis. The evaluation metrics used were accuracy, precision, recall, and the F1 score. Accuracy refers to the percentage of correctly classified steganography images out of the total number of images. Precision is the ratio of true positive results to the total number of positive results, which indicates the ability of a model to correctly identify steganography images. Recall is the ratio of true positive results to the total number of actual positive results, which indicates the ability of a model to detect all steganography images. The F1 score is the harmonic mean of the precision and the recall, and it provides a balance between the two metrics. The results showed that the CNN model had the highest accuracy, recall, and F1 score, and AlexNet, ResNet-50, and Inception had lower accuracy and recall scores. However, the precision of the CNN model was slightly lower than that of the other models. This means that the CNN model was better at correctly identifying the steganography images and detecting all of the steganography images, but it may also have had a higher false positive rate than the other models had. The following reasons may be why the CNN model was the most successful of the four methods. CNNs are very good at capturing spatial features from images, and this ability can also be useful for capturing sequential features from text data. In this case, the numerical representations of the text data could be treated as sequences of one-dimensional images, and a CNN could learn to capture relevant features from these sequences. Pre-trained CNN models are already trained on large-scale image classification tasks and have learned to recognize a wide range of features that are useful for many different image-related tasks, including feature extraction from text data.