## 5 Results
### Results Analysis  
In this subsection, we compare the results obtained for each optimization strategy employed. Figures 5-8 present bar graphs with the results obtained in terms of the accuracy and memory requirements of the optimized model for a selected algorithm at a fixed embedding rate. The accuracy is represented by the blue bars and the memory requirements by orange bars. The bars are arranged in descending order regarding the memory requirements. The left y-axis shows the accuracy value of the model, while the right y-axis shows the memory requirements in MB. Figure 5: Results in terms of accuracy and memory requirements of all employed optimization techniques for S-UNIWARD at 0.4 bpp. Figure 8: Results in terms of accuracy and memory requirements of all employed optimization techniques for WOW at 0.2 bpp. Figure 6: Results in terms of accuracy and memory requirements of all employed optimization techniques for S-UNIWARD at 0.2 bpp. Figure 7: Results in terms of accuracy and memory requirements of all employed optimization techniques for WOW at 0.4 bpp. Figure 5 shows all results regarding the accuracy and memory requirements of the optimization strategies employed for S-UNIWARD at 0.4 bpp. The best accuracy results were achieved by applying FP16 quantization, FP16 quantization combined with a pruning ratio of 50%, and weight clustering with both density-based and K-means++ centroid initialization. The memory space required by the optimized model when applying FP16 quantization was approximately twice that required when applying each of the other three aforementioned techniques. Considering the relation between accuracy and occupied memory savings, the best results were obtained by applying weight clustering with both K-means++ and density-based centroid initialization and by combining FP16 quantization with a 50% pruning ratio. The weight clustering with density-based centroid initialization proved to be the best choice for this scenario, with a slight increase in accuracy of 0.03% and with approximately 72% less memory required when compared with the original model. Figure 6 presents the achieved accuracy values and memory requirements of the optimized models when the scenario involved S-UNIWARD at 0.2 bpp. The accuracy of the original model (without the use of optimization strategies) was 73.60%. The results for five different optimized models showed accuracy values that were slightly better when compared with GBRAS-Net, along with savings in the memory required by the model. Accuracy results above 74% were obtained by applying FP16 quantization, a pruning scheme with a 70% ratio, and FP16 combined with a 50% pruning ratio. Considering the relation between the accuracy and the memory requirements of the model, the best results were obtained by applying FP16 quantization combined with pruning of 70%. The accuracy increased by 1.59% when compared to the original model. This strategy resulted in one of the five least space-demanding optimized models when the scenario involved S-UNIWARD at 0.2 bpp, with a size of 0.16 MB, which represents a saving of approximately 75% when compared with the original model. Figure 7 shows all results regarding the accuracy and memory requirements of the optimization strategies employed for WOW at 0.4 bpp. The accuracy of the original model for this scenario was 89.80%. Six different scenarios presented accuracy values above 89.00%. There was no loss of accuracy when FP16 quantization was applied. The memory saving for this strategy was approximately 52%. The optimized models with dynamic range quantization (0.16 MB) and weight clustering with random centroid initialization (0.13 MB) presented accuracy values of 89.05% and 89.20%, respectively, which represent accuracy losses below 1% when compared with that of GBRAS-Net, while they exhibited smaller memory requirements compared with the optimized model when using FP16 quantization. Figure 8 presents the achieved accuracy values and memory requirements of the optimized models for WOW at 0.2 bpp.