## 6 Conclusions  
This work addressed a variety of deep learning model optimization techniques, namely model pruning, quantization, and weight clustering, applied to a deep leaning method for image steganalysis (GBRAS-Net). It was observed that these strategies can lead to the same or even better results in terms of accuracy, while reducing the memory requirements for the model's storage by more than three times. By applying FP16 quantization, we achieved accuracy results that were slightly better than those of GBRAS-Net, while reducing the memory requirements of the model by approximately two times for both the S-UNIWARD and WOW algorithms at either 0.4 bpp or 0.2 bpp. By applying a weight clustering strategy, we achieved a model that was compressed by four times or more for all scenarios. For S-UNIWARD at 0.4 bpp, we achieved better accuracy results while reducing the memory requirements of the model by approximately four times compared to the original GBRAS-Net. For S-UNIWARD at 0.4 bpp and WOW at both 0.4 bpp and 0.2 bpp, we achieved accuracy results that were slightly worse than those of the original model. For WOW at 0.4 bpp, the decrease in accuracy was only 0.6%. The memory requirements of the compressed model were reduced by four times in this simulation. We assessed model pruning applied with eight different pruning ratios for both the S-UNIWARD and WOW steganography algorithms at 0.4 bpp and 0.2 bpp. The simulations showed that the more pruned the network, the lower the memory requirements. We achieved slightly better accuracy results when compared with GBRAS-Net for both S-UNIWARD and WOW at 0.2 bpp at a 60% or 70% pruning ratio, respectively. We combined both dynamic range and FP16 quantization with pruning to achieve even smaller occupied memory values. For S-UNIWARD at 0.2 bpp, accuracy of 75.19% was obtained, increasing by 1.59% when compared with the original GBRAS-Net, while the network was compressed four times. For the WOW algorithm, by applying this combination, we achieved the smallest network sizes. For WOW at 0.2 bpp, we were able to achieve a network with one eighth of the original GBRAS-Net's memory requirement. Figure 9: Memory requirements of all assessed optimization strategies. We compared the achieved results considering the accuracy impact and the memory savings of the model. For S-UNIWARD at 0.4 bpp, we highlighted the weight clustering strategy with K-means++ centroid initialization, which could maintain the accuracy while leading to a memory saving of 72% in comparison with the original model. For S-UNIWARD at 0.2 bpp, the best result was obtained by applying the combination of FP16 quantization with a pruning scheme at a 70% ratio. When the scenario involved the WOW algorithm at an embedding rate of 0.4 bpp, FP16 quantization proved to be the best choice in terms of accuracy, while, for greater model compression with a small impact on the accuracy, the best optimized models were those optimized via dynamic range quantization and weight clustering with linear centroid initialization. For an embedding rate of 0.2 bpp, the best results in terms of accuracy and model compression were achieved by applying dynamic range quantization and the combination of FP16 quantization and a pruning scheme at a 60% ratio. Although the implementation of the optimization techniques was focused on GBRASNet, there are other CNNs with greater network sizes. Future work could include addressing optimization strategies for these CNNs. It is important to highlight that there is room for improvement in weight clustering. In this work, four centroid initializations were considered. Other initialization strategies may be addressed or proposed. Future work could include the use of swarm intelligence approaches, e.g., [56], or modified versions of K-means or fuzzy K-means, e.g., [57; 58; 59; 60], for the purpose of weight clustering. Another approach is to investigate different values of the maximum number of clusters and their impacts in terms of the accuracy and memory requirements of the model.