# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 4 Results and Comparison with Existing Models
### Evaluation of the Proposed Model
This drop may be attributed to class imbalance or an increased sample diversity not fully captured during training, highlighting the importance of controlled dataset composition. With 10,000 images per class, the model regained stability and delivered 99.35% accuracy, 99.35% precision, and an F1-score of 99.35%, supported by smooth learning curves and a notable reduction in classification errors across both classes. Figure 4: The confusion matrix obtained for each case: (**a**) Confusion matrix using a dataset with 7000 images per class. (**b**) Confusion matrix using a dataset with 9000 images per class. (**c**) Confusion matrix using a dataset with 10,000 images per class. (**d**) Confusion matrix using a dataset with 12,500 images per class. Finally, for the largest set (12,500 images per class), a high-accuracy performance and 2500 hits per class were observed, with only two false positives and no false negatives, thus consolidating the highest accuracy value (99.96%), reflecting not only an excellent learning capacity but also robust behavior in large-scale conditions. These results confirm that increasing the volume of training data improves not only the precision but also the stability of the training process, as observed in the smoother loss and accuracy curves. In addition to these classic metrics, the receiver operating characteristic (ROC) curves were analyzed to evaluate the discriminative capacity of the model under different decision thresholds (see Figure 5). An ROC curve plots the true-positive rate against the false-positive rate, and the area under the curve (AUC) quantifies the overall classification quality. For the dataset with 5000 images per class, the AUC reached 0.9894, indicating a strong separation ability even with a moderate training volume. With 7000 and 10,000 images, the AUC values improved to 0.9941 and 0.9996, respectively, demonstrating greater robustness and convergence. Interestingly, the experiment with 9000 images showed a temporary drop  
Figure 5: The receiver operating characteristic curves obtained: (**a**) ROC obtained for a dataset with 7000 images per class. (**b**) ROC obtained for a dataset with 9000 images per class. (**c**) ROC obtained for a dataset with 10,000 images per class. (**d**) ROC obtained for a dataset with 12,500 images per class. in performance, yielding an AUC of 0.9417. This behavior may be attributed to variance in the validation subset or suboptimal tuning during training. Nevertheless, the best results were obtained with 12,500 images, where the AUC peaked at 0.9996, reflecting near-perfect separation between classes. Overall, the high AUC values across the experiments reaffirm the model's ability to distinguish between steganographic and non-steganographic images, complementing the precision and F1-scores obtained in earlier analyses. In Figure 5 the result of the ROC curve shows the effect of varying this threshold on the true-positive rate and the false-positive rate. In all cases, the area under the curve value was above 0.94, which implies that the model performed with high efficiency in terms of its ability to discriminate messages with grammatical meaning. Additionally, Figures 6-8 display examples of the semantic validation process applied to messages extracted from images classified as _With-steganography_. In these cases, the model correctly identified the presence of steganography and evaluated whether the retrieved message made sense in Spanish. This evaluation was performed using natural language processing techniques, and only those messages that exceeded a minimum threshold of linguistic coherence were considered true positives.