随机预言模型下可证适应性安全的门限FFS签名方案  

SAR image thresholding segmentation based on the bacteria foraging algorithm

作　　者：马苗[1] 梁建慧[1,2] 郭敏[1] 

机构地区：[1]陕西师范大学计算机科学学院,陕西西安710062 [2]海南大学应用科技学院,海南儋州571737

出　　处：《西安电子科技大学学报》2011年第6期152-158,178,共8页Journal of Xidian University

基　　金：先进计划与智能计算省部共建教育部重点实验室开放课题资助（ADIC2020002）,国家自然科学基金资助（60773121）

摘　　要：基于强RSA计算假设，提出了一种的随机预言模型下可证自适应安全的门限Feige-Fiat-Shamir 签名方案。该方案具有最优弹性，且计算量和通信量都较少。给出了该方案的严格安全性证明。在随机预言模型下，若基础的Feige-Fiat-Shamir签名方案在选择消息适应性攻击下是不可伪造的，且假设计算模大安全素数的离散对数问题是困难的，可证明该方案是不可伪造的、鲁棒和适应性安全的。In order to increase the speed and the accuracy of Synthetic Aperture Radar（SAR） image segmentation, a new thresholding segmentation method is proposed, which integrates the Bacterial Foraging Algorithm （BFA） and two-dimensional grey entropy. After the basic BFA is analyzed deeply, the method improves the search speed of the best threshold via shrinking the foraging space of the bacterial swarm. And then, an improved two-dimensional grey entropy model is regarded as the fitness function of our optimized BFA. Finally, the best threshold is located gradually and quickly by three behaviors of the bacterial swarm, i.e., chemotaxis, and reproduction, elimination and dispersal. Experimental results show that the proposed method is superior to some segmentation methods based on the Genetic Algorithm （GA） and Artificial Fish Swarm Algorithm （AFSA） in convergence, stability and segmentation effects.

关 键 词：门限密码学 数字签名 可验证秘密共享 可证明安全 适应性安全 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] N941.5[自动化与计算机技术—计算机科学与技术]