## 6 Conclusions and Future Work  
This paper presented a custom model designed and trained to classify original and colorized images. The proposed architecture is not very deep and makes use of a parallelism block with two convolutional layers, followed by a convolutional layer and three fully connected layers. According to tests performed for the hyperparameter selection, it was found that the Adam and SGD optimizers allow similar classifier performance, but that the RMSProp optimizer is not recommended for this type of task. Additionally, it was found that the input image size significantly affects the performance of the classifier, so this hyperparameter related to the network structure should be considered in any experimental tests of classification models. Finally, we evaluated the impact of four dropout values for the penultimate layer of the network and found that there is no linear relationship between performance and dropout value, so this hyperparameter should also be included in the test protocols.  
Additionally, we evaluated a model by transfer learning with the VGG-16 network, in which the pre-trained model was frozen down to the pooling_5 layer and only the fully-connected layers were modified and retrained. This model outperforms the custom model in terms of performance metrics, but it is 12x slower than the custom model. When these two models are compared with those existing in the state-of-the-art model, it is found that the proposed models are competitive in terms of generalization, improving on some of the results previously reported by other authors.  
Therefore, in applications that require a very high classification rate such as in the forensic field, the transfer-learning-based model is an excellent choice. However, in real-time applications or for massive image classification, the custom model is the one that is recommended.  
As future work we propose to evaluate other pre-trained models such as MobileNet, which could have shorter inference times than our VGG-16-based model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \begin{tabular}{c} **HTER** \\ **(Internal** \\ **Validation**) \\ \end{tabular} & \begin{tabular}{c} **HTER** \\ **(External** \\ **Validation**) \\ \end{tabular} & \
\begin{tabular}{c} **HTER’s** \\ **Difference** \\ \end{tabular} \\ \hline RecDeNet [25] &
\begin{tabular}{c} PASCAL 2012 \\ \end{tabular} & 12.6 & 22.3 & +9.7 \\ WISERNet I [14] & [23] + [27] + [28] & 0.95 & 22.5 & +21.55 \\ WISERNet II [15] & [23] + [27] + [28] & 0.89 & 31.7 & +30.81 \\ WISERNet III [26] & [23] + [27] + [28] & 0.89 & 4.7 & +3.81 \\ Custom model & [5] + [23] & 9.00 & 16.0 & +7 \\ VGG-16-based model & [5] + [23] & 2.60 & 2.9 & +0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed models with representative methods of the state-of-the-art approaches in terms of HTER (the lower the better).  
**Author Contributions:** Formal analysis, D.M.B. and D.R.; Funding acquisition, D.M.B. and D.R.; Investigation, C.U.; Methodology, D.M.B. and D.R.; Software, C.U.; Writing--Original draft, C.U. and D.M.B.; Writing--Review and editing, D.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by \"Vicerrectoria de Investigaciones--Universidad Militar Nueva Granada\", grant number IMP-ING-2936.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data CG-1050 used in this study are openly available in [6].  
**Conflicts of Interest:** The authors declare no conflict of interest.