共查询到19条相似文献,搜索用时 109 毫秒
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提出一种基于混沌序列的彩色图像加密算法。该算法首先应用二维Logistic混沌系统产生2个混沌序列,利用对2个混沌序列进行排序产生的2个编号序列对彩色图像进行位置的置乱,然后应用三维Lorenz混沌系统产生的混沌序列中各值进行大小排序,用以引导对置乱后的彩色图像进行像素点的R,G,B值的置换操作,从而实现对颜色的加密。实验结果表明,该算法具有密钥空间大、安全性高和保密性好的特点。 相似文献
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在混沌动力学系统的基础上,介绍了一种混沌二值序列的产生方法。利用非线性的方法产生的混沌二值序列具有较好的随机性和初值敏感性,同时,还给出了该方法在图像加密中的应用。实验表明,该方法具有较好的效率和安全性。 相似文献
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研究了一种基于提升小波变换与二维超混沌系统的图像加密方案.首先将图像进行整数提升小波变换,根据自适应排列置乱算法对提升小波变换后的低频子带系数进行调整置乱.然后由两个二维超混沌系统生成混沌序列并进行预处理,同时构造双混合反馈系统.最后利用双混合反馈系统对小波重构后的图像进行像素灰度值替代加密.仿真结果表明,该加密方案弥补了图像经传统小波变换后不能无失真重构的缺陷,并克服了单一的低维混沌系统保密性不高的缺点,在密钥空间和抵抗各种攻击方法上均有较好的改进,具有较高的安全性和实用性. 相似文献
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给出了一种混沌序列的非线性二值化方法,提出了一种基于位运算的数字图像加密算法.加密算法首先利用传统的混沌系统产生的混沌序列对图像进行位置置乱;其次对置乱后图像进行基于灰度值二进制序列的置乱操作;最后应用文中方法对结果图像灰度值的二进制序列按位进行异或运算.实验结果表明,该加密算法具有良好的安全性和加密效果. 相似文献
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提出了一种混沌神经网络加密压缩数字图像的算法.首先,将原始图像进行二维小波变换后,转换成小波域上的小波系数,然后对小波系数进行嵌入零树小波编码.接着,将码流中"1"的位置取出,存入一个矩阵中,对此矩阵进行混沌神经网络加密.该加密算法具有以下特征:速度快、无失真、安全性高.仿真试验结果表明,该算法能得到令人满意的结果. 相似文献
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混沌直扩信号扩频序列盲估计 总被引:2,自引:0,他引:2
与传统直扩序列相比,混沌扩频序列具有非线性复杂度较高的优点,该优点同时也是盲估计混沌扩频序列的难点。针对这个难点,该文提出了非线性弹性反传神经网络盲估计方法,充分利用非线性神经网络能逼近任意非线性函数的特性,无须搜索信息码和扩频序列之间的同步点,能在较低的信噪比下准确盲估计混沌扩频序列。传统的神经网络使用中,神经网络的有用信息是网络的输出,而该文中则是输出层的权系数。侦察截获的混沌直扩信号同时用作神经网络的输入和期望输出,神经网络收敛后的输出层权系数就是混沌扩频序列的估计值。仿真结果证明了该方法的有效性。 相似文献
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In this article, we propose sharpening the gain of the chaotic annealing neural network to solve 0- 1 constrained optimization problem. During the chaotic annealing, the gain of the neurons gradually increases and finally arrives at a large value. This strategy can accelerate the convergence of the network to the binary state and keep the satisfaction of the constrains. The simulations, which take the knapsack problems as examples,demonstrate that the approach is efficient both in approximating the global solution and the number of iterations. 相似文献
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针对目前混沌系统数字化后受硬件有限精度的影响,使得数字混沌0/1序列最终都将变成类短周期序列,降低了混沌加密系统的安全性。为克服此缺点本文将logistic混沌序列与Arnold变换相结合,提出了一种新型的logistic混沌序列算法,并通过实验对该算法产生的二值序列的自相关性、随机性等性能做了对比分析。同时利用此改进算法对数字图像进行加解密,结果表明新型的logistic混沌序列算法具有良好的安全性,能够满足保密通信的需要。 相似文献
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在软件漏洞检测领域,传统神经网络模型和图神经网络模型是已被验证的有效方法。目前,方案大多针对源代码进行漏洞检测,运用神经网络模型对二进制软件进行漏洞检测的研究相对较少,更是缺乏对图神经网络在二进制软件漏洞检测方面的研究。为充分研究神经网络模型在二进制软件漏洞检测方面的有效性,提出了一种基于复合式神经网络的二进制软件漏洞检测方法。将二进制代码向量化表示为同时支持传统神经网络模型和图神经网络模型训练的图数据结构;使用传统神经网络模型和图神经网络模型相结合的复合式神经网络模型对图数据结构进行学习和验证;在公开的二进制软件漏洞数据集上进行实验和对比分析,结果表明该方法能够有效提升漏洞检测能力,在准确率、精确度等性能指标方面都有明显提升。 相似文献
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为了扩展适合异步CDMA系统的扩谱序列,提高系统的性能,利用混沌序列的类随机、理想的自相关和互相关、易于产生并且数量多的特性,来生成适合异步码分多址系统的混沌扩谱序列。根据异步DS—CDMA系统的模型,分析适合异步CDMA系统的最优混沌扩谱序列的性能优点,提出选择和构造最优二进制混沌扩谱序列的关键指标,并与传统的伪随机序列(如Gold序列)进行数值仿真比较。结果表明应用最优混沌扩谱序列可以增加15%的系统容量。 相似文献
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The study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A neural network, the binary diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The binary diamond is a multilayer, feedforward neural network, which learns from examples in unsupervised one-shot mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done using a realistic database, consisting of approximately 90000 Landsat 4 Thematic Mapper pixels. The binary diamond and the nearest neighbor performances were close, with some advantages to the binary diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories and analyzing nonboundary pixels, are addressed and evaluated 相似文献
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A non-autonomous chaotic circuit which is suitable for high-frequency integrated circuit (IC) realization is presented. Simulation and experimental results verifying the feasibility of the circuit are given. We have numerically verified that the bit streams obtained from the stroboscopic Poincaré map of the system passed the four basic tests of FIPS-140-2 test suite. We also have verified that the binary data obtained from the hardware realization of this continuous-time chaotic oscillator in the same way pass the full NIST random number test suite. Then, in order to increase the output throughput and the statistical quality of the generated bit sequences, we propose a TRNG design which uses a dual oscillator architecture with the proposed continuous-time chaotic oscillator. Finally, we have experimentally verified that the binary data obtained by this oscillator sampling technique pass the tests of full NIST random number test suite without Von Neumann processing for a higher throughput speed while compared with the previous one where the proposed continuous-time chaotic oscillator is used alone. 相似文献
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M. Vidyasagar 《Circuits, Systems, and Signal Processing》1992,11(3):387-398
In this paper we study the problem of designing a neural network that gives the correct binary representation of a given real number. Previously this problem has been studied by Tank and Hopfield. The network proposed by them exhibits hysteresis in the sense that the current vector of the network sometimes converges towards a binary vector that isnot the correct binary representation of the input current. The reason for this is that the network proposed by them has multiple asymptotically stable equilibria. In the present paper, we propose another neural network which has the property that it hasa single, globally attractive equilibrium for almost all values of the input current. Hence, irrespective of the initial conditions of the network, the current vector converges towards the correct binary representation of the input current. 相似文献