一种基于演化细胞自动机的数据加密方法*

提出了一种演化密文与规则的加密方法,以细胞自动机的初态和规则作为演化对象,寻找满足经过迭代后能得到原文细胞自动机的初态和规则.某些原文只在某些特殊规则下才存在满足完全匹配的密文,而且匹配度越高搜索越耗时,所以引入了校验信息,以最大演化代数作为演化计算的终止条件,寻找迭代后与原文最匹配的密文,再通过校验获得完整的原文.通过实验得出:此种加密方法具有较大的密钥空间和良好的置乱效果,而且细胞自动机的邻居半径越大,演化计算的表现越稳定.     

Block cipher based on reversible cellular automata

We propose a new encryption algorithm relying on reversible cellular automata (CA). The behavior complexity of CA and their parallel nature makes them interesting candidates for cryptography. The proposed algorithm belongs to the class of symmetric key systems. Marcin Seredynski: He is a Ph.D. student at University of Luxembourg and Polish Academy of Sciences. He received his M.S. in 2004 from Faculty of Electronics and Information Technology in Warsaw University of Technology. His research interests include cryptography, cellular automata, nature inspired algorithms and network security. Currently he is working on intrusion detection algorithms for ad-hoc networks. Pascal Bouvry, Ph.D.: He earned his undergraduate degree in Economical & Social Sciences and his Master degree in Computer Science with distinction (’91) from the University of Namur, Belgium. He went on to obtain his Ph.D. degree (’94) in Computer Science with great distinction at the University of Grenoble (INPG), France. His research at the IMAG laboratory focussed on Mapping and scheduling task graphs onto Distributed Memory Parallel Computers. Next, he performed post-doctoral researches on coordination languages and multi-agent evolutionary computing at CWI in Amsterdam. He gained industrial experience as manager of the technology consultant team for FICS in the banking sector (Brussels, Belgium). Next, he worked as CEO and CTO of SDC (Ho Chi Minh city, Vietnam) in the telecom, semi-conductor and space industry. After that, He moved to Montreal Canada as VP Production of Lat45 and Development Director for MetaSolv Software in the telecom industry. He is currently serving as Professor in the group of Computer Science and Communications (CSC) of the Faculty of Sciences, Technology and Communications of Luxembourg University and he is heading the Intelligent & Adaptive Systems lab. His current research interests include: ad-hoc networks & grid-computing, evolutionary algorithms and multi-agent systems.     

Atmospheric dispersion modeling using Artificial Neural Network based cellular automata

Forecasting atmospheric dispersion in complex configurations is a current challenge in fluid dynamics in terms of calculation time and accuracy. CFD models provide good accuracy but require a great computation time. Simplified or empirical models are designed to quickly evaluate the dispersion but are not adapted to complex geometry. Cellular Automata coupled with an Artificial Neural Network (CA-ANN) are developed here to calculate the atmospheric dispersion of methane (CH₄) in 2D. Efforts are made in reducing computation time while keeping an acceptable accuracy. A CFD simulations database is created and the Advection-Diffusion Equation is discretized to provide variables for the ANN. Neural network design is made thanks to best sampling selection, architecture selection and optimized initialization. The coefficient of determination is over 0.7 for most cases of the test set despite small errors accumulated through time steps. CA-ANN is faster than CFD models by a factor from 1.5 to 120.     

基于二维概率元胞自动机的HBV动力学模型

为描述免疫系统中细胞微粒的多样性以及微粒运动和相互作用的随机性,在传统元胞自动机基础上,借用粗粒化思想,将多个状态变量引入元胞之中,并提出了“次级元胞”的概念,以表示不同类型的细胞粒子;应用该模型模拟乙型肝炎病毒（HBV）在无药物治疗情况下的自然感染和抗HBV药物药效比较,以及考察肝细胞感染率的影响因素,获得了较好的效果。     

Joint SPIHT compression and selective encryption

JCSE-SPIHT, an algorithm of joint compression and selective encryption based on set partitioning in hierarchical trees (SPIHT), is proposed to achieve image encryption and compression simultaneously. It can protect SPIHT compressed images by only fast scrambling a tiny portion of crucial data during the coding process while keeping all the virtues of SPIHT intact. Intensive experiments are conducted to validate and evaluate the proposed algorithm; the results show that the efficiency and the compression performance of JCSE-SPIHT are very close to original SPIHT. In security analysis, JCSE-SPIHT is proved to be immune to various attacks not only from traditional cryptanalysis, but also by utilizing sophisticated image processing techniques.     

基于混沌和细胞自动机的图像加密算法

为了设计加密性能好、容易实现的加密系统,充分利用混沌系统的密码学特性和细胞自动机良好的加密性能,提出了一种基于混沌系统和触发细胞自动机的图像加密算法.算法先利用混沌序列对图像进行加密处理;通过构造反转规则建立触发细胞自动机,并根据触发细胞自动机的迭代规则对图像进行二次加密.加密过程简单高效,且具有较大的密钥空间,可以保证系统的安全性.实验结果表明,该算法具有较好的加解密性能,且较小的邻域半径即可得到良好的加密效果,因此非常便于硬件实现.     

Double image compression and encryption scheme using logistic mapped convolution and cellular automata

Multimedia Tools and Applications - Due to the availability and increased usage of multimedia applications, features such as compression and security has gained more importance. Here, we propose a...     

基于JPEG2000的图像联合压缩加密算法

为实现图像的压缩和加密同步,在深入研究MQ算术编码器的基础上,将Lorenz混沌系统生成的随机序列作为流密钥对JPEG2000压缩算法中比特平面编码形成的上下文和判决进行修正,以改变MQ编码时的概率分布来使得输出的码流发生变化,从而实现图像联合压缩加密.阐述了上下文修正和判决修正加密算法的规则.对算法进行仿真,结果表明,相对于原始压缩算法,所提出的算法对图像的压缩效率几乎没有影响,且安全性好、实时性高.     

Improving the compression and encryption of images using FPGA-based cryptosystems

Compression and encryption technologies are important to the efficient solving of network bandwidth and security issues. A novel scheme, called the Image Compression Encryption Scheme (ICES), is presented. It combines the Haar Discrete Wavelet Transform (DWT), Significance-Linked Connected Component Analysis (SLCCA), and the Advance Encryption Standard (AES). Because of above reason the ICES efficiently reduce the overall processing time. This study develops a novel hardware system to compress and encrypt an image in real-time using an image compression encryption scheme. The proposed system exploits parallel processing to increase the throughout of the cryptosystem for Internet multimedia applications to implement the ICES. Using hardware acceleration for encryption and decryption, the FPGA implementation of DWT, SLCCA and the AES algorithm can be used. Using a pipeline structure, a very high data throughput of 330 Mbit/s at a clock frequency of 40 MHz was obtained. Therefore, the ICES is secure, fast and suited to high speed network protocols such as ATM (Asynchronous Transfer Mode), FDDI (Fiber Distributed Data Interface) or Internet multimedia applications. Shih-Ching Ou is working with the Department of Electrical Engineering, National Central University as a senior professor. His research interests include computer aided design, e-learning system, and virtual reality, etc. In August 2004, he serves as Leader University Professor and Director of Research and Development, now he act as Leader University Professor and Institute of Applied Information (Chairman). He has published a number of international journal and conferences papers related to these areas. Currently, he is the chief of Bioinformatics & CAD Laboratory. Hung-Yuan Chung joined the Department of Electrical Engineering at the National Central University, Chung-li, Taiwan as an associate professor in August 1987. Since August 1992, he was promoted as professor. In addition, he is a registered professional Engineer in R. O. C. He is a life member of the CIEE and the CIE. He received the outstanding Electrical Engineer award of the Chinese Institute of Electrical Engineering in October 2003. His research and teaching interests include System Theory and Control, Adaptive Control, Fuzzy Control, Neural Network Applications, and Microcomputer-Based Control Applications. Wen-Tsai Sung is a PhD candidate at Department of Electrical Engineering, National Central University in Taiwan. His research interests include computer aided design, web-based learning system, bioinformatics and virtual reality. He has published a number of international journal and conferences papers related to these areas. He received a BS degree from the Department of Industrial Education, National Taiwan Normal University, Taiwan in 1993 and received a MS degree from the Department of Electrical Engineering, National Central University, Taiwan in 2000. He has win the dragon thesis award; master degree thesis be recognized the most outstanding academic research. The thesis entitle is: “Integrated computer graphics system in a virtual environment.” Sponsor is Acer Foundation (Acer Universal Computer Co.). Currently, he is studying PhD at the Department of Electrical Engineering, National Central University as a researcher of Bioinformatics & CAD Laboratory.     

基于混沌细胞自动机的图像加密算法

提出了基于耦合混沌和触发细胞自动机的图像加密算法。首先用耦合混沌系统对图像进行加密,然后用耦合二维细胞自动机再次加密,最后用混沌序列对图像进行置乱。加密过程中同时运用了分组加密和流加密方法。仿真实验结果表明,该加密算法实现简单,扩散和混淆效果比较理想,有较强的抗攻击能力,具有良好的加密效果。     

多粒度可逆细胞自动机模型的数据加密方法

为了克服以往对细胞自动机的研究局限于单粒度细胞单元的问题,引入了多粒度细胞的概念,并结合可逆细胞自动机提出了一种基于多粒度可逆细胞自动机的加密算法.在该算法中,扩展的可逆细胞自动机具有足够多的规则,从而保证了安全的密钥空间;细胞单元通过"分裂-重组"实现自身粒度大小的动态调整,并依此来提高细胞自动机动力学特性的复杂性,实现了快速加、解密.同构的硬件结构使该算法具有很强的实用性,仿真实验表明,该加密算法具有较高的安全性.     

Compression-unimpaired batch-image encryption combining vector quantization and index compression

As the network bandwidth has grown rapidly, it has become common to share a large number of still images via the Internet by means of batch transmission. Unfortunately, most existing methods encrypt only a single image, so there is room for improvement in our ability to send batches of images at one time. A complete reconstruction of the methods is sometimes necessary, especially when considering performance criteria. This paper presents a novel batch-image encryption algorithm that combines Vector Quantization (VQ) and additional index-compression process to benefit from their computational efficiency and low transmission bandwidth without affecting the original compression rate. The experimental results show the performance of this new scheme in terms of compression rate and computational cost.     

Perceptual encryption with compression for secure vector map data processing

With the rapidly rising interest in geographic information system (GIS) contents, a large volume of valuable map data has been unlawfully distributed by pirates. Therefore, the secure storage and transmission of classified national digital map datasets have been increasingly threatened. As the importance of secure, large-volume map datasets has increased, vector map security techniques that focus on secure network and data encryption have been studied. These techniques are required to ensure access control and prevent illegal copying of digital maps. This paper presents perceptual encryption on the vector compression domain for copy protection and access control of vector maps. Our algorithm compresses all vector data of polylines and polygons by lossless minimum coding object (MCO) units and perceptually encrypts using two processes using the mean points and directions of MCOs. The first process changes the position of vector data by randomly permuting the mean points of MCOs, the so-called position encryption. The second process changes the geographic shape by circularly encrypting the directions of vertices in MCOs by the XOR operator. Experimental results have verified that our algorithm can encrypt GIS digital maps effectively and simply and can also improve the compression ratio, unlike general data encryption techniques, and thus, our algorithm is very effective for a large volume of GIS datasets.     

Text compression using two-dimensional cellular automata

This paper presents an elegant mathematical model using simple matrix algebra for characterising the behaviour of two-dimensional nearest neighbourhood linear cellular automata with periodic boundary conditions. Based on this mathematical model, the VLSI architecture of a Cellular Automata Machine (CAM) has been proposed for text compression. Experimental results of comparisons with adaptive Huffman coding scheme also presented.     

基本和混合元胞自动机的伪随机数发生器研究

针对基本元胞自动机（Cellular Automata,简称CA）、混合CA的伪随机数发生器进行了深入的研究,通过对比实验观察到混沌型基本CA输出的伪随机序列质量稳定并较优,而混合CA输出伪随机序列的相关性,尽管优于基本CA的平均表现,但远差于混沌型基本CA的表现。针对混合CA的伪随机数发生器,提出了一种基于混合CA与粒子群优化（Particle Swarm Optimization,简称PSO）算法融合的伪随机数产生算法。在该算法中,元胞对应于PSO的粒子,每个元胞按照各自不同的规则进行迭代演化,其对应粒子在迭代规则空间中飞行。该算法通过计算每个元胞产生伪随机序列的熵值作为粒子的适应度函数值,有效地实现每个元胞最佳规则的搜索,一定程度上提高了混合CA产生伪随机序列的质量。给出了基于小生境技术、构造出最优CA-PSO耦合伪随机数发生器的研究方向。     

具有鲁棒性与数据嵌入能力的JPEG压缩图像加密算法

针对JPEG图像加密算法引起的比特流大小增加与鲁棒性较差的问题,提出了一种具有鲁棒性与数据嵌入能力的JPEG图像格式兼容的加密算法。首先,利用AC系数抓取纹理信息并生成部分区域,基于该区域将DC系数编码;然后,使用8个不同的顺序扫描AC系数,选择其中比特流大小最小的顺序;之后,提取每块的AC系数,并且操作AC系数来增加置换的范围。算法的主要过程均具有可逆性,可成功提取嵌入信息并且完美地重建原图像。基于真实JPEG照片的实验结果表明,本算法对素描攻击具有鲁棒性,可抑制比特流大小增加且具有数据嵌入的能力。     

Asynchronous cellular learning automata

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous cellular learning automata) is needed. In this paper, we introduce asynchronous cellular learning automata and study its steady state behavior. Then an application of this new model to cellular networks has been presented.     

基于细胞神经网络超混沌特性的图像加密新算法

针对一般流密码对明文变化不敏感的缺陷,基于细胞神经网络（CNN）,提出一种图像加密新算法。以一个6维CNN产生的超混沌系统作为密钥源,并根据明文图像各点像素值的逻辑运算结果选取密钥;同时使用像素位置置乱和像素值替代两种方法对数字图像进行加密。实验表明,该算法加密效果好,NPCR值和密钥敏感性高(>0.996),满足数字图像加密安全性的要求,同时具有计算简单、易于实现、能提高数字图像传输的安全性等特点。     

基于模糊元胞自动机的电网故障演化模型

为了构建与实际电网更加接近的电网故障模型,将模糊理论及元胞自动机(CA)理论相结合,提出并建立了用于模拟电网故障演化的模糊元胞自动机模型,并定义了模型中元胞状态、电网状态及故障传递程度的模糊规则库,同时进行了基于IEEE39节点系统的电网故障传播演化过程仿真。仿真结果进一步验证了电网的自组织临界性(SOC),同时结果表明,该模型较不采用模糊规则的电网故障模型其损失负荷幂律特性曲线斜率绝对值增大了17%,电网更加稳定,该模型与电网实际运行情况更加接近。     

基于JPEG-LS压缩比控制的图像压缩加密算法

针对传统图像压缩比控制不精细及低维混沌系统保密性不高的问题,提出一种基于连续色调静态图像的无损或近无损压缩标准(JPEG-LS)压缩比控制的图像压缩加密算法.在深入分析JPEG-LS中失真控制参数Near对图像压缩比和重建质量的影响的基础上,首先,对光栅扫描的图像数据进行梯度处理;然后,比较梯度值与Near的大小关系以决定进入游程模式进行游长编码或常规模式进行Golomb编码;再次对三维Lorenz混沌系统生成的序列进行随机性处理,采用该序列作为密钥分别对游程模式、常规模式和全模式(游程和常规两种模式)下的压缩码流进行加密;最后,对Near进行实时动态调整,实现了对图像的压缩比精细控制且提高了保密性.仿真结果表明,所提算法能够实现良好的压缩比控制,且重建图像质量比线性压缩比控制算法提高了大约0.5 dB;同时算法安全性高,能够有效抵抗熵攻击、差分攻击、穷举攻击、统计攻击等多种攻击,且加密对压缩效率基本没有影响.