DNA Computing For RGB Image Encryption With Genetic Algorithm

Abstract

A combination of DNA computing and a genetic algorithm is announced for RGB image encryption. The model is strong based on the scrambling technique of DNA computing operations using the crossover and mutation process and establishing a dynamic key based on a genetic algorithm, including a set of parameters such as population size, number of generation and mutation probability. First, the decoding of the image GA selected DNA sequence encoding process and the random key for the three R G B channels were followed by the DNA addition process. The decoded DNA added to the matrix of the output. Finally, conduct the XOR-mod procedure on the decoded matrix and the random number of the genetic algorithm to obtain the encrypted image. The paper includes countless experimental steps to confirm that the model has a high degree of safety and strength against different types of attacks.

Introduction

Recently data safety has become a major problem for the government, private and defense organizations owing to large information losses from illegal information access. The appreciated data from illegal readers can be protected by using numerous cryptographic techniques. Development increases rapidly in the information security field by using innovative and different techniques, which assurance both of accessibility, completeness, and confidentiality [1] [2].

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Cryptography is the practice of hiding and manipulating data to be transmitted over a network by complex or logical mathematics to shield info against opponents and make it visible only to the recipient [3].

All creatures on this planet are made of the same type of genetic blueprint coded inside the cells of the human body containing a whole unit of Deoxyribo Nucleic Acid (DNA) containing dual-stranded nucleotide helix which holding the code of the inherited cell information. DNA is produced by building chemical blocks that are recognized as nucleotides that collect into three parts: phosphate group, sugar group, nitrogen bases. The DNA element is formed by linking nucleotides to a chain using uneven phosphate and sugar, while the Nitrogen bases are Adenine, Thymine, Cytosine, and Guanine [4] [5] [6].

DNA computing begins in 1994 and started a new information epoch based on features found in DNA for huge parallelism, vast storage, and ultra-low consumption of power. DNA cryptography developed by DNA computing in the context of an innovative cryptography technique, where DNA acts as a data carrier that considers modern biological technology [7] [8].

Genetic algorithm is a randomized approach to search and optimization based on the principle of natural selection structures motivated via Darwin's theory of development. There are three primary operators in the genetic algorithms: selection, crossover, and mutation. Depending on some criteria, the operation's that they joined a loop called generation stopped. The strongest search system for genetic algorithms is reproduction coupled with crossover operation [9] [10] [11].

The paper's structure is as follows. Section 2 includes certain similar work in the area of image encryption on DNA computing. Section 3 introduces DNA computing. Section 4 describes the suggested model. The results of security shall be explored in Section 5. Section 6 suggests, at last.

Related Work

An additional DNA sequence method for the encryption of pictures was viewed in [12]. Two additional DNA activities scramble each gray picture pixel and complement DNA. The DNA sequence image encoding is then divided into identical blocks which are combined by adding DNA, and as a result of the two logistic maps, the DNA structure is incorporated into the blocks. The model has accomplished excellent outcomes in both encryption and potential attack resistance.

Image encryption dealing with four different types of chaotic maps and additional DNA mixed with noise introduced in [13]. To get the encrypted image, the image first encoded with DNA then applied two chaotic maps, then applied DNA addition together with two other chaotic maps joined with noise effect. Among the other maps, the cross chaotic map gives the best results.

DNA subsequence operations plus chaotic maps are suggested in [14]. DNA processes are used in combination with a logistic map to fumble the picture pixel. The model is capable of resisting high-protection comprehensive and statistical attacks

For the encryption image, a DNA sequence genetic algorithm is introduced in [15]. Using GA, the image pixel is converted to decrease the pixel correlation and then replaced with random DNA substrings that consider the key to XOR activity attaining the substitution stage. The model against attacks of all types is fast and easy and robust.

DNA Computing

DNA Encoding and Decoding

DNA stores data as a file consisting of four organic seats: adenine (A), guanine (G), cytosine (C), and thymine (T). DNA seats are coupled, A with T and C with G, to process base pairs components that suit one another. There are couples in 0 and 1 of binary arithmetic, so 00 and 11 are couples, 01 and 10 are also couples. [16] [17]. For each pixel, the binary value of the four biological units of DNA is: A is equal to 00, C is equal to 01, G is equal to 10, and T is equal to 11 every 8 bit [18].

DNA Sequences Regulations for Addition and Subtraction

DNA sequences have several functions, including insertion, transformation, addition, and subtraction. Table 1 and Table 2 show the guidelines for addition and subtraction [19] [20].

From the apparent outcomes it appears that any change even a slight change to the main picture would result in a greater chance of the encoded image which makes the suggested model is competent and strongly resisting the differential attack.

CONCLUSION

The suggested model is very quick and meets the cryptography principles criteria. The system utilizes the addition of the DNA sequence to scramble the image's three RGB channels pixels based on a random key and utilizes XOR-plus-mod as a confusing procedure by using the genetic algorithm process as a pseudo-random number generation, besides using the crossover and mutation process with DNA decoding for more security, resistance to various attacks, such as differential and statistical attacks, is strongly increased. The scheme is easy, robust and appropriate for image encryption, plus has no problems in mathematics.

References

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3. Stallings, W., Cryptography and network security principles and practice (5th ed.), Boston: Pearson, 2014.
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10. K. F. Man, Member, K. S. Tang, S. Kwong. “Genetic Algorithms: Concepts and Applications”, In: IEEE Transaction On Industrial Electronics, Vol. 43, No.5; October 1996, pp. 519 – 534.