Image encryption for offshore wind power based on 2D-LCLM and Zhou Yi eight trigrams

IF 1.7 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Bio-Inspired Computation Pub Date : 2023-01-01 DOI:10.1504/ijbic.2023.133505

Lei Kou, Jinbo Wu, Fangfang Zhang, Peng Ji, Wende Ke, Junhe Wan, Hailin Liu, Yang Li, Quande Yuan

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引用次数: 12

Abstract

Offshore wind power is an important part of the new power system. Due to the complex and changing situation in the oceans, its normal operation and maintenance cannot be done without information such as images; therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi eight trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm is resistant to common attacks and has prefect encryption performance.

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基于2D-LCLM和周易八字的海上风电图像加密

海上风电是新型电力系统的重要组成部分。由于海洋形势复杂多变，其正常运行维护离不开图像等信息;因此，在信息传递的过程中，传递正确的图像就显得尤为重要。本文提出了一种基于二维滞后复杂逻辑映射(2D-LCLM)和周易八元组的海上风电加密算法。首先，通过Sha-256构造2D-LCLM的初始值，将2D-LCLM与明文相关联。其次，根据周易八卦图提出了一种新的加密规则，对像素值进行模糊处理并生成轮密钥;然后，将2D-LCLM与Zigzag结合形成s盒。最后，对该算法进行了仿真实验。实验结果表明，该算法能够抵抗常见的攻击，具有良好的加密性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bio-Inspired Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.10

自引率

5.70%

发文量

审稿时长

>12 weeks

期刊介绍： IJBIC discusses the new bio-inspired computation methodologies derived from the animal and plant world, such as new algorithms mimicking the wolf schooling, the plant survival process, etc. Topics covered include: -New bio-inspired methodologies coming from creatures living in nature artificial society- physical/chemical phenomena- New bio-inspired methodology analysis tools, e.g. rough sets, stochastic processes- Brain-inspired methods: models and algorithms- Bio-inspired computation with big data: algorithms and structures- Applications associated with bio-inspired methodologies, e.g. bioinformatics.