Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and networks. Thus, the progress of image encryption techniques is becoming an increasingly serious issue and considerable problem. Small space of the key, encryption-based low confidentiality, low key sensitivity, and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this study. In our proposed scheme, a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption scheme. In the confusion stage, different scrambling operations related to the original plain image pixels are designed using Chen’s map. A stream pixel scrambling operation related to the plain image is constructed. Then, a block scrambling-based image encryption-related stream pixel scrambled image is designed. In the diffusion stage, two rounds of pixel diffusion are generated related to the confusing image for intra-image diffusion. Chen’s map, logistic map, and DNA computing are employed to construct diffusion operations. A reverse complementary rule is applied to obtain a new form of DNA. A Chen’s map is used to produce a pseudorandom DNA sequence, and then another DNA form is constructed from a reverse pseudorandom DNA sequence. Finally, the XOR operation is performed multiple times to obtain the encrypted image. According to the simulation of experiments and security analysis, this approach extends the key space, has great sensitivity, and is able to withstand various typical attacks. An adequate encryption effect is achieved by the proposed algorithm, which can simultaneously decrease the correlation between adjacent pixels by making it near zero, also the information entropy is increased. The number of pixels changing rate (NPCR) and the unified average change intensity (UACI) both are very near to optimal values.

In the past few years, there has been a huge change in how information is shared around the world. Online communication (through a variety of platforms) has slowly become an important way to share information and keeping data safe has become one of the most important issues [

Image encryption can be done in different ways, such as by permuting, substituting, shuffling, confusing, and diffusing. Diffusion is a very common operation due to its easy implementation and the good results it produces. The purpose of diffusion is to change the pixel’s value in an image [

Proposed a confusion method based on multi-stream scrambling, the plain image has been separated into four major blocks during the confusion stage. A 2 × 128 block is extracted from each of the primary blocks to perform the scrambling operation. The confusion method applies a multi-stream scrambling algorithm to the plain image based on several iterations.

Proposed a diffusion method based on DNA coding rules. For the diffusion to work, two chaotic sequences are generated by using the Chen’s mapping and Logistic Map on the sequence value received from the confusion image. The image encryption is completed by performing a DNA XOR operation on the DNA molecules between the confused DNA and the obtained key matrix from Chen’s map. Reverse DNA Complementary Rule and XOR operations are presented in this work to perform the diffusion process.

Previous algorithms can be classified into two groups based on the features of the scrambling algorithm. The first group is to keep unchanged the size of the original image. Some researchers changed the plain image by extending the Arnold map. In particular, the initial values based on the chaotic map are the coordinates of the pixels. The new locations of the pixels are found by repeating the chaotic system. According to [

Reference [

Examining some of the image encryption techniques described above that are based on scrambling reveals that these algorithms have the following security flaws. The majority of the algorithms that they proposed are conventional scrambling and diffusion-mode techniques. The original plain image is first scrambled, and after that, to change the value of pixels, it is diffused. Because of this, the image needs to be processed in two processes to produce the desired result; also, the level of safety performance is diminished. Moreover, some developed techniques are insufficient to withstand chosen-plain image assaults and are insensitive to tiny variations in the original image. For example, certain encryption approaches have been broken in recent years [

This work proposed an image encryption approach based on confusion-diffusion techniques. The algorithm uses different key streams based on different chaotic systems, namely, Chen’s map and the logistic map. Based on that, three-dimensional and one-dimensional hyperchaotic systems are used in this study. Then, confusion-diffusion phases are built based on scrambling and DNA computing. In the confusion phase, a multi-stream scrambling method is presented to confuse the original plain image using two different generated chaotic sequences. Then, the confused image is diffused based on the DNA concept and an XOR operation using two different generated chaotic sequences. Finally, based on confusing and diffusing the plain image, the encrypted image is obtained. The encryption approach in this study not only encrypts the plain image securely but also ensures the excellent performance of the proposed encryption algorithm. This section presents the related materials and details of the proposed algorithm.

This section describes the proposed image encryption algorithm. Confusion and diffusion are the two main phases of the proposed algorithm. The encryption algorithm consists of two stages: encryption at the stream pixel scrambling level and encryption at the block scrambling level. The Chen’s chaotic map is employed to conduct stream pixel scrambling and block scrambling. The confusion stage selects an appropriate window size of

While researching chaotic feedback control in 1999, Chen’s chaotic system has been discovered as a system that had dynamic characteristics and was complicated. The equation that represents the mathematical model of Chen’s chaotic system is as follows [

where the parameters of Chen’s chaotic system have been denoted as

A common and reasonably straightforward one-dimensional discrete chaotic map called a logistic map is defined as

The significant degree of correlation that exists between neighboring pixels is one of the most crucial aspects of an image. Thus, the main goal of the confusion stage is to break the connection between pixels next to each other by moving them horizontally and vertically. However, if all the connections between pixels in a plain image have to be broken, a large matrix of new positions has to be constructed, and this takes a lot of computing time and resources. During the confusion process, the location of each image pixel will change to break the relationship between the plain image and the encrypted image. By using the confusion process, it seems like the key is not just related to the cipher image. Each pixel in the encrypted image should depend on a different part of the key. The confusion method is made up of two main parts: the generation of dynamic keys and the process of confusing plain images. The first step is called the image stream pixel, and its purpose is to reorder the image’s pixels by shuffling them and severing their connections with their neighbors. Alternatively, this process can dissolve the correlation that exists between neighboring plain image pixels. This process consists of two different subprocesses, which are referred to respectively as initial dynamic key generation and pixel streaming. Utilizing Chen’s map, the dynamic key is constructed. In the first step, the original plain image of size M × N is divided into four main blocks of the same size. Then, an appropriate window size has been selected from each main block for the stream pixel process.

where

The process of streaming pixels is continued by combining selected blocks to generate a matrix with a size of 128 × 8. Then, the matrix is converted into a one-dimensional array with a size of 1028 when

Scrambling is the procedure of rearranging the pixels of an image by changing the digital image in a certain way. Based on an analysis of the effects of the scrambling process, the conventional scrambling encryption algorithm is not secure enough. It is more secure when combined with other methods to make hybrid encryption. In this paper, another scrambling process has been performed to make a hybrid scrambling process. The scrambled image from the previous stage was chosen to carry out the block scrambling process based on a dynamic random key that was generated using Chen’s chaotic map. In the previous operation, the plain image, which has a dimension of 256 × 256, is segregated into four main subblocks. The dimension of each subblock is considered 128 × 128. Subsequently, each is defused using the previous process. The defused sub-blocks are obtained after several iterations, where defusing sub-blocks is performed in different iterations. Due to taking further sub-blocks with 128 × 2 in the first iteration, a 128 × 8 matrix is generated and defused. At the second iteration in each sub-block, another sub-block of size 128 × 4 is taken. Thus, in this way, the defused sub-blocks in the first iteration are defused one more time in the second iteration. Furthermore, the number of iterations that have been performed on the defused image for the scrambling process is different, and the obtained scrambled image at each iteration is also different. Because we follow

When the confusion part is implemented, the pixels next to each other in the image will have less of a link to each other. Because their positions will be changed randomly, which minimizes the correlation between the adjacent pixels. On the other hand, the histogram of the scrambled image will still be the same as the histogram of the original image since the pixel values have not changed. At this level, the only thing that change is the position of the pixel. Thus, the diffusion phase is another process of the proposed algorithm to change the value of pixels.

To build a robust encryption algorithm with the robust performance of histogram and information entropy, the diffusion encryption method to encrypt the confusing image is performed. The diffusion is applied to the confused image, which is obtained in Sub-Section 3.2. The diffusion process exploits the DNA computing concept to encrypt the image. This process means that the pixel values in an image can be changed in an adequate manner. This helps in diffusing the frequencies of the confused image through many pixel values of the encrypted image (diffused image). To obtain an encrypted image with no statistical features, for example, histogram or information entropy. To produce a meaningful statistical attack, significantly more encrypted images are required. A diffusion process was used to randomly alter pixel values. Chen’s chaotic and logistic maps are used to generate diffuse dynamic random keys. In this study, DNA coding rules are used to convert confusing images into DNA. Then, reverse DNA complementary rules are applied to the confusing image to perform the first level of the diffusion process. An XOR operator between the new form of DNA after reverse complementary rules and the diffusion random key was used to diffuse the image.

DNA computing is a type of parallel computing system that was invented by Leonard Adleman. In DNA computing, four nucleic acids are used to express the information, which is referred to as adenine (A), cytosine (C), guanine (G), and thymine (T) [

Rule | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|

00 | C | T | A | G | G | A | T | C |

01 | T | C | G | A | T | C | G | A |

10 | A | G | C | T | A | G | C | T |

11 | G | A | T | C | C | T | A | G |

Assume that a pixel value in a confusing image is 78 in decimal and that its binary representation is (78)_{10} = (01001110)_{2}. Then, it is DNA coding was performed according to mode 7, and the sequence “GTAC” was obtained. After that, when we convert the obtained sequence based on another mode, for example, mode 2, (10001101)_{2} = (141)_{10} can be obtained. It is clear that by encoding and decoding DNA in its most basic form, a value can be altered dramatically, which results in an improved level of security for digital images.

There are two ways to encrypt an image based on DNA sequence complement, which is the complementary method based on a single base and the method that utilizes the biotechnology principle of matching single bases and double bases to implement the complementary process. This study uses the first complementary rule, which is described in

DNA base | Complement | DNA base | Complement |
---|---|---|---|

T | A | G | C |

A | T | C | G |

DNA bases (A and T) are complemented pairs whereas (C and G) are other complement pairs. The corresponding binary complement is satisfied when both the complement of 00 is 11 and that of 01 is 10, and vice versa. This study used the idea of single complementary rules after encoding the confusing image to DNA form. Where the T base is converted to A and that G is converted to C, and vice versa. In addition, to increase the security level and to change the pixel values significantly, an inverse complementary method is performed. For example, after obtaining the GTCA sequence from the value of (210)_{10} using mode 1, this sequence is converted to CAGT and then reversed as TGAC. Thus, instead of obtaining (11010010)_{2} of binary sequence from (210)_{10} 0r (120)_{10} = (01111000)_{2}. The value is changed more than one time by obtaining (45)_{10} = (00101101)_{2}. Thus, this process changes the values from 210 to 120 and then finally to 45.

The rule that the DNA procedure is premised on is that every two binary values equal one DNA base. There are eight qualified ways to code DNA, and each way has a set of algorithms. This means that each common method is the same as one of eight different DNA algorithms.

⊕ | A | T | G | C |
---|---|---|---|---|

A | A | T | G | C |

T | T | A | C | G |

G | G | C | A | T |

C | C | G | T | A |

To perform the DNA XOR operation, this study generates a dynamic random key using the Chen’s chaotic system, denoted as

In this section, the performance and simulation findings were presented, along with presenting comparisons with recently proposed image encryption techniques. MATLAB R2018a software was used to perform manipulations on the experimental to assess the performance of the proposed algorithm. The memory for installation is 8 GB, and the operating system is Windows 10; the CPU is an Intel(R) Core (TM) i7-1065G7 running at 1.50 GHz. Several standard images have been utilized to assess the performance of the proposed algorithm using different tests.

In this study, to validate and ensure the robustness of our proposed algorithm, we used nine standard images that are available publicly. Used images are listed in

Image | Size | Image | Size | Image | Size |
---|---|---|---|---|---|

Lena | 256 × 256 | Airplane | 256 × 256 | Boat | 256 × 256 |

Baboon | 256 × 256 | House | 256 × 256 | Peppers | 256 × 256 |

Cameraman | 256 × 256 | Barbara | 256 × 256 | Pentagon | 256 × 256 |

A workable image encryption method is necessary to possess a key space that is big enough as well as a key that is sensitive enough to be able to withstand brute force attacks. The analysis of the key space and the security key-based sensitivity has been conducted in this subsection.

The key space is a representation of the total number of possible combinations that could be used for the security key. A brute-force assault is one of the most prevalent types of cyberattacks. In this type of attack, an adversary attempts to guess the correct security key by exhaustively scanning the key space of an encryption algorithm. Therefore, to protect the algorithm from an assault using brute force, having a key space that is sufficiently large is one of the primary criteria that can guarantee higher safety [^{15}. This contributes to (246.51)^{4} possible guesses of the value ^{186} possible values of ^{15}) from Chen’s system is used in the diffusion process. As a result, the proposed algorithm is capable of withstanding any kind of brute-force assault.

Analysis | Proposed | Ref. [ |
Ref. [ |
Ref. [ |
Ref. [ |
Ref. [ |
Ref. [ |
Ref. [ |
---|---|---|---|---|---|---|---|---|

Key space size | 2^{186} |
2^{170} |
2^{256} |
10^{98} |
2^{159} |
2^{72} |
2^{256} |
2^{170} |

An ideal method for encrypting images ought to be sensitive enough to the security key. This means that even a slight variation in the security keys should provide a completely different result when the image is decrypted. The degree of secrecy that may be maintained by a key is an essential component of any reliable encryption method. This means that even a small shift in the key would result in a very noticeable shift in the output, and this phenomenon can be analyzed using two different aspects. First, during the process of encrypting an image if the same image is encrypted with a key that is even slightly various than the original key, a completely various encrypted image will be produced. Second, during the process of encrypting an image, if ever there is the smallest variance exists between both keys of encryption and decryption, the encrypted image cannot be successfully decrypted. The initial condition and parameter with 10^{15} are particularly important considerations for double chaotic maps due to their high degree of sensitivity. To begin, a key sensitivity test is performed by encrypting the same images using a key that is only marginally distinct from the initial key which determines how sensitive the key is.

The first row from ^{−15} or 10^{−10}, it is impossible to achieve the plain image. Moreover, if even a single key from the multiple keys is changed, the plain image will no longer be decryptable. Thus, it is clear that even a relatively minor change can have a significant impact.

The development of a cryptosystem requires extensive statistical analysis such as histogram analysis, entropy analysis, and correlation analysis. The perfect algorithm for encrypting images should be able to defend against several types of statistical assaults.

The histogram in grayscale is easier to understand and has good visibility. It is possible to deduce from the figure, the probability of occurrence of the gray value as well as its frequency. The effect of encryption is improved when the histogram is more evenly distributed. The histogram of gray levels displays each possible level of gray as well as the frequency with which that level of gray occurs [

In graphic histograms, the standard deviation and the variance are measurements of dispersion used to support the findings of visual inspection. They gauge the degree to which the components of a collection of data differ from one another around the mean. The mean (average value) of the two datasets may be the same, but the variances may be very different [

The variance determines the average variance between each value’s deviation from its center

The intensity values of the histogram-based frequency for the grayscale image from 0–255 are indicated by

Histogram-based standard deviation is represented by

Algorithm | Image | ||
---|---|---|---|

Plain image | 41.264 | 199.3 | |

Ref. [ |
Plain image | 40.975 | 202.4 |

Encrypted image | 228.7 | 15.1 | |

Ref. [ |
Plain image | 38.451 | 196 |

Encrypted image | 414 | 20 |

In addition to the graphical examination of the encrypted image’s histogram distribution, we employ the chi-square test to demonstrate that the encrypted image has a uniform histogram distribution. This is done so that we can demonstrate the uniformity of the encrypted image in a manner that is more accurate, less value of this test shows better uniformity. The chi-square test provides the following justification for the equation that was utilized in the process of calculating the proposed encryption method’s effect on the histogram’s uniformity:

The number of gray values is represented by

Image | Chi-square | Image | Chi-square | Image | Chi-square | Image | Chi-square | Image | Chi-square |
---|---|---|---|---|---|---|---|---|---|

Lena | 238.25 | Baboon | 247.84 | Peppers | 247.19 | Airplane | 245.84 | Cameraman | 223.10 |

As a result of the strong correlation that exists between adjacent pixels in the image, to defend against the statistical attack. The correlation that exists between adjacent pixels should be minimized to the greatest extent possible. The degree of correlation between pixels can be characterized by using a statistic known as the correlation coefficient. There is a significant association between adjacent pixels in the plain image in all directions. The image that has been processed by the encryption technique will only be resistant to statistical attacks if the correlation coefficient of neighboring pixels in the encrypted image is sufficiently low. Calculations are made to determine the correlation coefficients between the original image and the encrypted image using neighboring pixels that have been chosen at random from each direction. Analyses are performed to determine the correlation between neighboring pixels in the plain image and the cipher image. The correlation coefficient _{xy}

According to the formula presented above,

The correlation between two variables is said to be weaker when the absolute value of the correlation coefficient is smaller.

Image | Algorithm | Direction | ||
---|---|---|---|---|

Horizontal | Vertical | Diagonal | ||

Lena | Plain image | 0.901413 | 0.939524 | 0.937249 |

Ref. [ |
−0.000312 | −0.001682 | 0.002213 | |

Ref. [ |
0.002030 | 0.010543 | 0.001985 | |

Ref. [ |
−0.002125 | 0.000912 | 0.000347 | |

Ref. [ |
−0.000264 | 0.005245 | 0.001881 | |

Cameraman | Plain image | 0.886564 | 0.888024 | 0.917474 |

Ref. [ |
0.007065 | −0.0004 | 0.000546 | |

Ref. [ |
0.0026387 | 0.010641 | −0.000148 | |

Baboon | Plain image | 0.610995 | 0.568495 | 0.702473 |

Ref. [ |
−0.002285 | −0.0064 | −0.002322 | |

Ref. [ |
−0.014249 | 0.007364 | 0.006820 | |

Peppers | Plain image | 0.822548 | 0.844933 | 0.888428 |

Ref. [ |
−0.002056 | −0.004612 | −0.007053 | |

Ref. [ |
−0.002391 | −0.000961 | −0.004767 |

To compare the correlation between adjacent location data values of the original plain image and the encrypted same image in a manner that is more easily understood by the human eye.

The technique that is proposed in this study is contrasted with the algorithms that have been proposed in other similar literature on image encryption.

There are different measures that can be used to measure the randomness of pixels whereas the most common and fundamental is information entropy. Mathematically information entropy can be calculated in

The probability of the grey level value

Image | Algorithm | Result | Image | Algorithm | Result | Image | Algorithm | Result |
---|---|---|---|---|---|---|---|---|

Lena | Plain image | 7.4868 | Pepper | Plain image | 7.5016 | Camer-man | Plain image | 6.9380 |

Ref. [ |
7.9973 | Ref. [ |
7.9975 | Ref. [ |
7.9974 | |||

Ref. [ |
7.9971 | Ref. [ |
7.9974 | Ref. [ |
7.9970 | |||

Ref. [ |
7.9974 | Ref. [ |
7.9974 | Ref. [ |
7.9976 | |||

Ref. [ |
7.9972 | Ref. [ |
AES | 7.8761 | ||||

Ref. [ |
9.9971 | AES | 7.8734 | Baboon | Plain image | 7.3898 | ||

Ref. [ |
7.9086 | House | Plain image | 7.2855 | ||||

AES | 7.8693 | Ref. [ |
7.9971 | |||||

Boat | Plain image | 7.1714 | Ref. [ |
7.9974 | Ref. [ |
7.9972 | ||

Ref. [ |
7.9975 | Ref. [ |
7.9968 | |||||

Ref. [ |
7.9975 | Airplane | Plain image | 6.7813 | Barbara | Plain image | 6.5100 | |

Ref. [ |
7.9972 |

Moreover, for local information entropy, the image has been selected based on the non-overlapping blocks _{1}, B_{2}, B_{3}, …, B_{n}

where

Image | Result | Image | Result | Image | Result | Image | Result |
---|---|---|---|---|---|---|---|

Lena | 7.9773 | Pepper | 7.9582 | Cameraman | 7.9495 | Boat | 7.9631 |

House | 7.9403 | Airplane | 7.9594 | Baboon | 7.9702 | Barbara | 7.9707 |

An objective measure for judging the quality of images is the peak signal-to-noise ratio, and the mathematical equation for determining this value is as follows:

where N × N represents the size of the image,

Image | PSNR | Image | PSNR | Image | PSNR | Image | PSNR | Image | PSNR |
---|---|---|---|---|---|---|---|---|---|

Lena | 8.74 | Baboon | 10.21 | Peppers | 9.18 | Airplane | 10.77 | Cameraman | 9.21 |

Image analysis frequently makes use of and benefits from the application of the differential attack analysis method. The differential attack is a very popular and successful analytical method in image analysis. The goal of a differential attack is to investigate how a very small alteration in a plain image can have a significant impact on the corresponding encrypted image. Any slight modifications, even if a bit altered in an original plain image, will result in entirely new encrypted images. This is an essential property that a decent encryption method should have in order to withstand differential assaults. The number of pixels changes rate (NPCR) and the unified average changing intensity (UACI) are two of the most common indices used to quantify the performance of (resisting) withstanding differential attacks in image encryption. In this context, NPCR represents the proportion of various gray values of various encrypted images at the same location. However, UACI represents the average alert density of various encrypted images, NPCR and UACI can be calculated using _{1}_{2}

Image | Algorithm | NPCR | UACI | Image | Algorithm | NPCR | UACI |
---|---|---|---|---|---|---|---|

33.4475 | |||||||

Ref. [ |
99.6178 | 33.4647 | Ref. [ |
99.6100 | 33.6700 | ||

Lena | Ref. [ |
99.6093 | 33.5267 | Cameraman | Ref. [ |
99.6071 | 33.4766 |

Ref. [ |
99.5987 | 31.2188 | Ref. [ |
99.6002 | 33.3921 | ||

Ref. [ |
33.4412 | Ref. [ |
99.7200 | 33.6400 | |||

AES | 99.6156 | 33.5032 | AES | 99.6021 | 33.5265 | ||

Algorithm | NPCR | UACI | |||||

33.4684 | |||||||

Baboon | Ref. [ |
99.6070 | 33.3620 | ||||

Ref. [ |
99.6030 | 33.6318 | |||||

Ref. [ |
99.6170 | 33.4790 |

To demonstrate that the chaotic system-generated sequence complies with the SP800-22 standard created by the National Institute of Standards fits the characteristics of a random sequence, and fulfills the necessary random standard (NIST). The 3 test items from the NIST Statistical Test Suite are used to determine if the generated sequence is random or not. We shall state that the sequence is not random if the

NIST test | |
---|---|

Frequency | 0.253551 |

Block frequency | 0.213309 |

Runs | 0.619772 |

In real-world applications, the performance of the cryptosystem in terms of both its ability to protect data and how quickly it can process data is a significant metric. In addition to the concern for safety, time is also an essential quality in an image encryption method. The proposed approach is obviously the confusion-diffusion method, and it is made up of a procedure for scrambling the data at the bit level and the block level, as well as a procedure for diffusing the data at the pixel level. Therefore, we demonstrate the speed performance of the system using the confusion time and the diffusion time. In this part of the analysis, the time required for execution is measured in seconds.

Image | Time | Image | Time | Image | Time |
---|---|---|---|---|---|

Lena | 0.43 | Baboon | 0.38 | Cameraman | 0.41 |

This paper proposes a new method for conducting encryption at the pixel level and block level. The proposed method is based on confusion, which is formed by proposing a stream pixel scrambling algorithm, and diffusion, which is formed by exploiting the DNA concept. The proposed method in this study is based on the use of a double chaos structure. This study exploits the three-dimensional chaos-based Chen’s system and one-dimensional Logistics for ciphering plain images by chaos. The proposed method for image encryption has advantages based on different structures of chaos, being more secure, robust against attackers, and key space. The confusion process in this study has been performed based on Chen’s chaotic system. However, the diffusion process is carried out based on the combination of Chen’s chaotic system and Logistic Map, which utilizes the technology of DNA coding concept to achieve image encryption. By performing the confusion and diffusion process for image encryption based on combining double chaotic systems, the capability against the encryption attackers was developed.

In addition, as is seen in

To ensure the security and privacy of digital images, a new image encryption technique-based confusion-diffusion with double chaotic maps namely the Chen’s map and Logistic map is proposed. Confusion is performed based on two stages: scrambling-based stream pixel level and scrambling-based blocking structure. In confusing plain image-related scrambling, the generated sequence of three-dimensional chaotic-based Chen’s maps is controlled. This helps to improve the sensitivity of the plain image as well as its resistance to differential attack. One of the main features of this algorithm is different scrambling operations based on different random chaotic sequences used in confusion. Furthermore, DNA computing is used to build the diffusion stage. It is carried out based on two stages: diffusing-based reverse complementary rule and diffusion-based multi-XOR operation. These operations are carried out using Chen’s chaotic map and the Logistic map, which are based on generated random sequences. This stage can withstand the proposed algorithm from statistical attack strongly. Because the diffusion operation is carried out in two rounds based on randomly generated sequences, it can improve the sensitivity of the proposed algorithm to the plain image. Moreover, to evaluate the proposed algorithm, we have evaluated our algorithm using several standard images including Lena, Baboon, Peppers, Barbara, Cameraman, Boat, Airplane, House, and Pentagon. In addition, to evaluate the performance of our proposed image encryption algorithm, several common analyses are used including Information Entropy, NPCR, and UACI. This algorithm has achieved 7.9975, 99.6189, and 33.4691 as the value of Information Entropy, NPCR, and UACI for image Lena. The results demonstrated that the proposed image encryption algorithm has superb security performance. Image encryption techniques based on DNA computing and chaos systems are still under constant research, also many problems are still required to be further addressed and solved. Multi-image combination encryption, as well as image encoding based on the parallel DNA concept, is considered our next focus to improve the image encryption algorithm.

The authors extend their appreciation to the Deanship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the Project Number: IFP22UQU4400257DSR031.

The authors declare that they have no conflicts of interest to report regarding the present study.