摘要：

为使图像加密系统具备优化功能,并解决当前遗传算法无法实现全局最优、收敛速率慢等问题,提出奇偶 树型交互学习机耦合全局离散遗传算法的密文优化系统。定义权值更新机制,耦合混沌映射,构造奇偶树型交互 学习机及其互扰模型。将切断型轮盘赌择取机制引入均匀交叉算子中,以图像分块的相邻像素相关系数和密文信 息熵为目标,根据权重理论设计加权适应度函数,提出一种全局离散遗传算法,最终形成“初始加密-密文优化”的 加密结构。实验结果表明,与超混沌算法、离散遗传算法、元胞自动机相比,该系统的加密质量较好,并且具备全局 优化功能,可优化所有迭代结果,使最终输出密文的信息熵最大,相关系数最小。

关键词: 奇偶树型交互学习机, 离散遗传算法, 均匀交叉算子, 轮盘赌择取机制, 混沌映射, 加密优化

Abstract:

In order to make the encryption system have the optimization performance,and solve these problems such as not achieving the global optimization and low speed of convergence,the cipher text optimization system based on the Tree Parity Interactive Learning Machine(TPILM) and discrete evolution algorithm is proposed in this paper. It defines the weight update mechanism,and couples the chaotic mappings to construct the TPILM and its mutual interference model. It introduces the cutting roulette selection mechanism into the uniform crossover operator. Meanwhile,it takes the adjacent pixels correlation coefficient and the cipher text information entropy of image block,introduces the weight theory to design the fitness function to propose a novel global discrete evolutionary algorithm for firstly applying to image encryption. At last,it produces the encryption structure of “initial optimization-cipher optimization”. Experimental results show that,compared with other encryption systems,the encryption system in this paper has the best quality and the function of global fast optimization to optimize all the iterative outcomes to make the cipher have the maximum information entropy and the lowest correlation coefficient.

Key words: Tree Parity Interactive Learning Machine(TPILM), discrete genetic algorithm, uniform crossover operator, roulette selection mechanism, chaotic mapping, encryption optimization

中图分类号: 

• TP391