## 4 Conclusions  
In this paper, an image data protection algorithm based on a multilevel encryption scheme and automated-selection mechanism was developed to further protect personal and commercial data uploaded to cloud servers for real-time applications, monitoring, and transmission. The main contributions of this paper are summarized as follows: (**1**) The automated-selection mechanism is crucial for the proposed scheme. The size of the mechanism that chooses the subimage directly affects the system performance and efficiency. Furthermore, thanks to this mechanism, high-resolution images can be easy to cipher, with high-level encryption obtained at the end. The automatic mechanism is also known as the sliding pixel window, which expedites the two-step process, that is, the confusion and diffusion stages. The confusion stage was designed to drastically change data from plain image to cipher image. To help in this stage, pixel conversion from decimal to binary and its vertical and horizontal relocation are performed not only to randomly move bits, but also to change the pixel values when they return to their corresponding decimal values. (**2**) The diffusion stage was designed to destroy all possible patterns in the sliding pixel window still present after the confusion stage. Two hyperchaotic systems with a logistic map (multilevel scheme) produce pseudorandom numbers to separately conceal the original data of each subplain image in the first- and second-level encryption processes. Security analysis was performed on the final result to demonstrate that the proposed image encryption algorithm makes brute-force attacks infeasible. Another key feature is its high security level. (**3**) The proposed method can be useful in communication systems, and it can be further improved to be implemented in real time. Different hyperchaotic systems can also be combined and used in the two-stage scheme to improve the encryption algorithm. As future research, a new layer can be added to automatically create suitable initial conditions for correct chaos iteration.  
Conceptualization, S.-Y.L. and C.-S.C.; methodology, S.-Y.L., C.-S.C., M.A.B.H. and L.-M.T.; software, S.-Y.L. and M.A.B.H.; validation, M.A.B.H. and C.-S.C.; formal analysis, S.-Y.L., L.-M.T. and C.-S.C.; investigation, M.A.B.H. and L.-M.T.; resources, S.-Y.L. and L.-M.T.; writing--original draft preparation, M.A.B.H. and S.-Y.L.; writing--review and editing, S.-Y.L., M.A.B.H. and C.-S.C.; visualization, S.-Y.L., M.A.B.H., and L.-M.T.; supervision, S.-Y.L. and C.-S.C.  
This research was funded in part by the Ministry of Science and Technology (MOST 107-2628-E-027-003-MY3, MOST 108-2221-E-027-094) and in part by the Institute for Development and Quality, Macao.  
We deeply appreciate the big support from the Ministry of Science and Technology, National Taipei University of Technology, Taiwan, and the important support from the research team of the Institute for Development and Quality, Macao.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**[PERSON] Image (849 × 600)** & **Lena Image (512 × 512)** & \\ \hline
**Plain Image** & **Ciphered Image** & **Plain Image** & **Ciphered Image** \\ \hline
6.425890 & 7.999634 & 7.235559 & 7.999362 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Information-entropy analysis.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Wonder Woman (2880 × 1800)** & **Lena (256 × 256)** & **[40]** & **[41]** & **[42]** & **[44]** \\ \hline
7.999965 & 7.99993 & 7.9895792 & 7.9974 & 7.9972 & 7.9970 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of information entropy analysis for the Lena image (\(256\times 256\)).  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.