# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 5 Conclusions  
The aim of this paper is to provide researchers and practitioners who are new to the field of image encryption with an introduction to image encryption and help enable them to grasp the current state of image encryption techniques and evaluation metrics. In this paper, a comprehensive review of several cutting-edge image encryption algorithms is conducted, focusing on their respective strengths and weaknesses. We categorized image encryption algorithms into seven distinct classifications based on the techniques they employ, that is, chaotic systems, DNA encoding, neural networks, compressive sensing, frequency domain, and meaningful sensing. Furthermore, this paper explores the security, quality, and efficiency metrics used in the literature to evaluate image encryption algorithms, such as correlation coefficient, histogram analysis, entropy, gray-level co-occurrence matrix (GLCM) analysis, mean square error (MSE), peak signal-to-noise ratio (PSNR), unified average change intensity (UACI), number of pixels change rate (NPCR), NIST SP 800-22 test suite, and more.  
Although this paper elaborates on many of the prominent image encryption algorithms and provides a good review of the current state-of-the-art image encryption techniques and evaluation metrics, this paper is not an exhaustive list of all the image encryption algorithms. One of the future research directions will be to explore other image encryption algorithms not discussed in this paper, such as optical image encryption and quantum image encryption. Another future research direction will be to conduct security and complexity analyses of the prominent image encryption algorithms. Another future research  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Approach** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{Traditional} & Well-established; Secure; Standardized and tested & High computational requirement; Slow encryption speed; Faulty decryption of cipher images with corrupted pixels \\ \hline \multirow{2}{*}{Chaotic systems} & Fast computation; Minimal complexity & Small range of chaotic behavior \\ \hline \multirow{2}{*}{DNA} & High security; Fewer storage requirements & Experimental and only explored in academic research Require specialized lab equipment \\ \hline \multirow{2}{*}{Neural networks} & Fast encryption & Security level not explored \\ \hline \multirow{2}{*}{Frequency domain} & Intuitively secure & Computationally intensive; Security analysis has not been explored \\ \hline \multirow{2}{*}{Compressive sensing} & High security; Small size of cipher image & High computational overhead \\ \hline \multirow{2}{*}{Meaningful encryption} & High security due to combining encryption and steganography & High computational overhead \\ \hline \hline \end{tabular}
\end{table}
Table 6: Strengths and weaknesses of different image encryption approaches.  
direction will be to develop newer evaluation metrics for evaluating the security of different image encryption algorithms.  
Conceptualization, Y.A. and A.M.; methodology, Y.A. and A.M.; software, Y.A.; validation, Y.A.; formal analysis, Y.A. and A.M.; investigation, A.M.; resources, A.M.; data curation, Y.A.; writing--original draft preparation, Y.A. and A.M.; writing--review and editing, Y.A. and A.M.; visualization, Y.A. and A.M.; supervision, A.M.; project administration, A.M.; funding acquisition, Y.A. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.