Minocha Sachin, Sharma Suvita Rani, Singh Birmohan, Gandomi Amir H
School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.
Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India.
Sci Rep. 2025 Mar 19;15(1):9476. doi: 10.1038/s41598-025-86569-9.
Chaos-based encryption methods have gained popularity due to the unique properties of chaos. The performance of chaos-based encryption methods is highly impacted by the values of initial and control parameters. Therefore, this work proposes Iterative Cosine operator-based Hippopotamus Optimization (ICO-HO) to select optimal parameters for chaotic maps, which is further used to design an adaptive image encryption approach. ICO-HO algorithm improves the Hippopotamus Optimization (HO) by integrating a new phase (Phase 4) to update the position of the hippopotamus. ICO-HO updates the position of hippopotamuses using ICO and opposition-based learning, which enhances the exploration and exploitation capabilities of the HO algorithm. ICO-HO algorithm's better performance is signified by the Friedman mean rank test applied to mean values obtained on the CEC-2017 benchmark functions. The ICO-HO algorithm is utilized to optimize the parameters of PWLCM and PWCM chaotic maps to generate a secret key in the confusion and diffusion phases of image encryption. The performance of the proposed encryption approach is evaluated on grayscale, RGB, and hyperspectral medical images of different modalities, bit depth, and sizes. Different analyses, such as visual analysis, statistical attack analysis, differential attack analysis, and quantitative analysis, have been utilized to assess the effectiveness of the proposed encryption approach. The higher NPCR and UACI values, i.e., 99.60% and 33.40%, respectively, ensure security against differential attacks. Furthermore, the proposed encryption approach is compared with five state-of-the-art encryption techniques available in the literature and six similar metaheuristic techniques using NPCR, UACI, entropy, and correlation coefficient. The proposed methods exhibit 7.9995 and 15.8124 entropy values on 8-bit and 16-bit images, respectively, which is better than all other stated methods, resulting in improved image encryption with high randomness.
基于混沌的加密方法因其混沌的独特特性而受到欢迎。基于混沌的加密方法的性能受到初始参数和控制参数值的高度影响。因此,这项工作提出了基于迭代余弦算子的河马优化算法(ICO-HO)来为混沌映射选择最优参数,并进一步用于设计一种自适应图像加密方法。ICO-HO算法通过集成一个新的阶段(阶段4)来更新河马的位置,从而改进了河马优化算法(HO)。ICO-HO使用迭代余弦算子和基于反向学习来更新河马的位置,这增强了HO算法的探索和开发能力。应用于CEC-2017基准函数获得平均值的Friedman平均秩检验表明ICO-HO算法具有更好的性能。ICO-HO算法用于优化分段线性混沌映射(PWLCM)和分段混沌映射(PWCM)的参数,以在图像加密的置乱和扩散阶段生成密钥。所提出的加密方法的性能在不同模态、位深度和大小的灰度、RGB和高光谱医学图像上进行了评估。利用不同的分析方法,如视觉分析、统计攻击分析、差分攻击分析和定量分析,来评估所提出的加密方法的有效性。较高的归一化像素变换误差(NPCR)和统一平均变化强度(UACI)值,即分别为99.60%和33.40%,确保了对差分攻击的安全性。此外,所提出的加密方法与文献中现有的五种先进加密技术以及六种类似的元启发式技术在NPCR、UACI、熵和相关系数方面进行了比较。所提出的方法在8位和16位图像上分别表现出7.9995和15.8124的熵值,优于所有其他所述方法,从而实现了具有高随机性的改进图像加密。