## 5 Results
### Encrypted Images with Noise  
Below, the results explore the images encrypted with damage after noise application. The experiment considers Figures 4 and 5. While Figure 4 shows the result of the following procedure. The AICLDH algorithm encrypts [PERSON] image in Figure 2. Then, the encrypted image is applied with the multiplicative noise of size 40% and the figure encrypted with damage is decrypted with AICLDH. Finally, Figure 5 is the result of the same previous procedure, with only one difference. The original image and the one encrypted with damage are encrypted and decrypted with the AES-CBC algorithm, respectively.  
In Section 2, the median filter 5 \(\times\) 5 was described to improve the encryption process with noise. In this way, Figure 5 presents the [PERSON] image encrypted and decrypted with AICLDH after the \(\chi^{2}\) noise application at 40%. In contrast, Figure 5 shows the [PERSON] image after the median filter 5 \(\times\) 5 application to the deciphered image with damage. Consequently, it is possible to observe how the sharpness of the damaged image improves.  
On the other hand, Table 12 shows the SP parameter evaluations. It is when the images of Figure 2 encrypted with AICLDH were damaged with the \(\chi^{2}\) noise of different sizes. Finally, Table 13 shows the following results. The images were encrypted with AICLDH in Figure 2, after a 40% size damage applied using the different types of noise proposed in this research, and then the decrypted images with noise after the 5 \(\times\) 5 filter application are shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color** & **Peppers** & **Donkey** & **Lena** & **Barbara** \\ \hline Red & 272.9/\(\checkmark\) & 274.2/\(\checkmark\) & 276.3/\(\checkmark\) & 239.9/\(\checkmark\) \\ Green & 275.2/\(\checkmark\) & 241.1/\(\checkmark\) & 220.7/\(\checkmark\) & 273.1/\(\checkmark\) \\ Blue & 240.2/\(\checkmark\) & 251.6/\(\checkmark\) & 262.3/\(\checkmark\) & 274.2/\(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the Goodness-of-Fit test (\(\checkmark\) Accept, x Reject), with \(\alpha=0.01\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Color** & **Black Image** & **White Image** \\ \hline \multirow{4}{*}{AC} & Red & 50.004 & 49.957 \\  & Green & 50.027 & 49.952 \\  & Blue & 49.990 & 49.968 \\ \hline \multirow{4}{*}{NPCR} & Red & 99.617 & 99.599 \\  & Green & 99.595 & 99.603 \\  & Blue & 99.590 & 99.598 \\ \hline \multirow{4}{*}{UACI} & Red & 33.522 & 33.421 \\  & Green & 33.418 & 33.452 \\ \cline{1-1}  & Blue & 33.506 & 33.407 \\ \hline \hline \end{tabular}
\end{table}
Table 11: AC, NPCR, and UACI values for the completely black and completely white images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Color** & **Peppers** & **Donkey** & **Lena** & **Barbara** \\ \hline Red & 0.296/\(\checkmark\) & 0.707/\(\checkmark\) & 0.674/\(\checkmark\) & 0.759/\(\checkmark\) \\ Green & 0.072/\(\checkmark\) & 0.064/\(\checkmark\) & 0.864/\(\checkmark\) & 0.551/\(\checkmark\) \\ Blue & 0.151/\(\checkmark\) & 0.602/\(\checkmark\) & 0.256/\(\checkmark\) & 0.174/\(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: The randomness measurement using the Discrete Fourier Transform (\(\checkmark\) Accept, x Reject), with \(\alpha=0.01\).  
Figure 4: Peppers image deciphered with AICLDH: (**a**) Original image; (**b**) Deciphered image when a multiplicative noise of 40% size was applied after encryption.  
Figure 5: Peppers image deciphered with AES-CBC: (**a**) Original image; (**b**) Deciphered image when a multiplicative noise of 40% size was applied after encryption.  
Figure 6: Peppers image deciphered: (**a**) Just deciphered with noise and AICLDH; (**b**) Deciphered and filtered using median filter of 5 \(\times\) 5.