层叠式并行图像Hash函数的结构及其算法实现

Design and implementation of cascaded structure for parallel image hash functions

传统Hash函数采用链式结构,不能充分利用图形和图像的二维特征来提高处理速度,更难以支持并行计算。为克服这2个缺点,提出了一种Hash函数结构,其在并行计算平台上的时间复杂度仅为o(logn)。分析了该结构相关的基本问题,并设计了在该结构下基于细胞神经网络实现的Hash函数。实验结果表明该Hash函数具有优异的敏感性、随机性和抗碰撞能力。

The traditional Hash functions use a property of graphics or images. The chain-like computing platform. A new structure of Hash time complexity is as low as o（logn） on a chain-like structure, which can not make best use of the 2D structure is low in efficiency when implemented on a parallel function is proposed to overcome these shortcomings and the parallel computing platform. Some fundamental problems regarding the structure are analyzed. With this structure, a Hash function based on cellular neural network is proposed, which shows satisfactory randomness sensitivity to input and resistance to collision with simulation experiments.