Correcting Errors in Image Encryption Based on DNA Coding

Wang, Bin; Xie, Yingjie; Zhou, Shihua; Zheng, Xuedong; Zhou, Changjun

doi:10.3390/molecules23081878

Cited by 21 publications

(8 citation statements)

References 49 publications

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“…This should promote further development of DNA storage, using our successful improvement of this algorithm. In addition, the constructed reliable DNA coding set can also be applied to other fields of DNA, such as DNA neural network computing model [56], DNA coding image encryption [57], DNA parallel computing model [58,59], Using the DNA storage architecture to make embedded storage materials [60], etc., which has a wide range of application value.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

Self Cite

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“…Our method can achieve the same code rate with a shorter sequence length, so it is efficient and more competitive than altruistic algorithm in DNA storage. In other aspects, DNA coding image encryption [48] and some neural-like computing models, see e.g. neural networks [49], [50] and parallel computing models [51], [52] can be considered to design reliable DNA code set in DNA storage.…”

Section: Discussionmentioning

confidence: 99%

K-Means Multi-Verse Optimizer (KMVO) Algorithm to Construct DNA Storage Codes

et al. 2020

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In an era of information explosion, dealing with massive data has become a problem. Since DeoxyriboNucleic Acid (DNA) is a high-density storage medium with long storage endurance, a DNA based storage system is a viable solution. The first consideration of a DNA storage system is the DNA codes, which can avoid non-specific hybridization of DNA strands in the hybridization reaction process by using related constraints, such as Hamming distance constraints, GC-content constraints, and no-runlength constraints. A K-means Multi-Verse Optimizer (KMVO) algorithm is proposed to construct a better code boundary than the previous Multi-Verse Optimizer (MVO) algorithm that satisfies the above constraints. Our results can store information more efficiently over a given length, increasing storage utilization. INDEX TERMS DNA code design, DNA storage, k-means, MVO algorithm.

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“…Inspired by DNA sequences with the biological properties such as parallel computation and low energy consumption, DNA computation and DNA coding are widely used in image encryption [53]. In this issue, Wang et al [54] introduced their new algorithm for correcting image encryption errors by using DNA coding. Hamming distance was used to reduce the similarity of DNA sequences for error correcting.…”

Section: Bio-inspired Researchmentioning

confidence: 99%

Molecular Computing and Bioinformatics

Liang

Zhu

et al. 2019

Molecules

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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

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Correcting Errors in Image Encryption Based on DNA Coding

Cited by 21 publications

References 49 publications

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

K-Means Multi-Verse Optimizer (KMVO) Algorithm to Construct DNA Storage Codes

Molecular Computing and Bioinformatics

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