一种基于抖动和混沌技术的数字图像篡改检测及修复算法

本文提出了一种基于抖动和混沌技术的数字图像篡改检测及修复算法.该算法使用小波变换后的低频子图和抖动技术生成图像的认证及修复信息,在有效减少水印数据量的同时,将水印嵌入小波变换后的高频子图,从而达到水印的不可见性.运用混沌技术完成水印的嵌入和加密,并结合中国余数定理,进一步减少水印嵌入对图像质量的影响.实验证明,该算法兼顾了水印的不可见性和鲁棒性,并且能够一定程度修复篡改图像,在图像认证和修复方面具有较高的实用意义.     

三维可逆混沌映射的图像加密算法

提出了一种三维可逆混沌映射图像加密算法。基于Line map二维混沌可逆映射,推导了该三维可逆映射的数学表达式。将灰度图像用一个三维矩阵数据描述,并按照所提出的算法将其组成一个二维的二进制图像。首先对此图像应用Line map二维混沌可逆映射进行像素置乱处理,然后再将置乱后的二进制图像还原成十进制的灰度图像,这样就得到了加密后的图像。所提出的方法可以通过一次三维可逆混沌映射同时实现图像加密的两个步骤,即像素置乱和像素混淆。仿真实验结果表明了该算法的有效性,且加密速度快、安全性高、简单易行。     

保守超混沌在数字图像加密中的应用

基于保守超混沌信号，提出一种数字图像加密算法.该算法利用一个5维保守超混沌系统产生5通道时间序列对原图像分别进行RGB三基色像素级和比特级置乱，再对置乱的RGB三基色作异或操作.在此基础上，利用其中一个通道的时间序列作为密钥分别进行一次正向异或操作的扩散和S盒处理，一次反向异或操作的扩散与S盒处理及置乱，得到加密的图像.最后利用直方图、信息熵、密钥空间等安全性指标对该加密算法进行测试，并与一个五维耗散超混沌系统应用于图像加密的实例进行对比.数值结果表明保守混沌应用到数字图像加密的算法具有更高的安全性和可靠性.     

基于深度神经网络的图像修复取证算法

提出一种基于深度神经网络的图像修复取证算法,该算法可通过编码器网络自动提取图像修复遗留的痕迹特征,通过解码器网络预测像素类别,从而判断出图像是否经过修复篡改以及修复篡改的区域。同时,采用特征金字塔网络对解码器网络中的特征图进行信息补充。采用MIT Place数据集作为训练集,UCID数据集作为测试集,对训练集和测试集分别使用了不同的修复篡改算法。实验结果表明,与其他图像修复取证算法相比,所提算法的修复区域定位更精准,处理速度更快,且对不同的修复篡改方法具有较好的稳健性和较强的泛化能力。     

一种基于保守混沌的密钥分发协议及图像加密算法

耗散混沌系统可以通过时滞嵌入法重构混沌吸引子,因而耗散混沌在基于混沌的信息加密技术中存在一定隐患。针对这一问题提出一种基于保守混沌的密钥分发协议及图像加密算法,该算法将图像数据通过Hash算法转换为保守混沌系统的初始值,形成一次一密的加密结构。然后利用保守混沌信号结合密钥分发协议生成二进制密钥流,该过程由发送方和接受方双方共同完成。使用得到的二进制密钥流对图像数据置乱和扩散,得到加密图像数据。仿真结果表明：该算法安全可靠,能够有效地隐藏原图像信息,并能抵御一些常见的攻击。此外,该加密算法所使用的保守混沌不存在吸引子难以被破解,算法的安全性进一步加强。     

基于混沌的自嵌入安全水印算法

利用混沌系统的伪随机性和初值敏感性，提出一种基于混沌的自嵌入安全水印算法.该算法以混沌初值为密钥生成混沌序列，根据混沌序列的索引有序序列随机生成图像块的水印嵌入位置.与现有的自嵌入算法相比，该算法实现了水印嵌入位置的随机选取，有效扩大了算法的密钥空间，且解决了自嵌入水印算法如何准确定位篡改块的问题.理论分析和仿真结果表明，该算法不仅提高了自嵌入水印算法的篡改定位的能力，而且进一步增强了算法抵抗向量量化攻击和同步伪造攻击的能力. 关键词： 数字水印 混沌 脆弱水印 自嵌入     

基于灰度变换的红外图像实时分割算法

针对高灰度等级红外数字图像提出了一种基于灰度变换的实时分割算法.通过实时分析原始图像中几个小区域的灰度直方图,自动确定分割阈值,将原始图像通过灰度变换实时转换为低灰度等级图像.该算法能够将高灰度等级的红外数字图像实时显示在普通显示器上,并且能够有效抑制背景并保留目标灰度信息.由于变换后的图像灰度等级较低而且背景及噪音信息较弱,极大地提高了红外跟踪器的性能.     

一种基于超混沌系统的立体图像零水印算法

为了在不改变立体图像质量的前提下对立体图像实施版权保护,提出了一种基于超混沌离散系统的立体图像零水印算法. 该算法利用立体图像左右视点小波变换域低频子带视差的稳定性以及离散余弦变换直流系数稳定的特点构造了一种视差零水印. 在水印构造过程中,利用了超混沌离散系统的初始值敏感性、参数密钥空间大和动力学行为复杂等特性映射视差零水印的 位置信息,从而增强了水印算法的安全性.此外,还分析了水印安全性与水印容量之间的关系. 实验结果表明,该立体图像视差零水印算法对加噪、滤波、压缩、剪切、图像放大和缩小等各种对称和非对称攻击表现出了 较强的鲁棒性.     

基于数字图像处理技术的人脸检测算法研究

为了提高人脸检测的准确性及检测速度,需要对基于数字图像处理技术的人脸检测算法进行研究。使用当前方法进行人脸检测时,需要提取脸部特征数目较多、检测速度过慢,降低人脸检测效率。为此,提出一种基于数字图像处理技术的人脸检测算法。该方法首先获取人脸数字图像,通过拉开数字图像的灰度间距,使数字图像灰度均匀分布,进而提高数字图像对比度,使图像更加清晰,再通过Wiener维纳滤算法对处理后的数字图像进行图像平滑去噪,在此基础上使用Robert边缘检测算子方法对数字图像人脸边缘每个像素点检测,得到数字图像中人脸边缘的基本图像,将其输入到计算机数字图像处理系统中进行识别检测。实验仿真证明,所提算法在检测速度及准确性等方面具有明显的优势。     

基于物理混沌的混合图像加密系统研究

初步实现了基于物理混沌的混沌和数据加密标准算法级联的混合图像加密系统,基于该系统研究了级联加密与单级加密的抗统计分析能力,以及不可预测性强弱不同的混沌信号在该系统中应用时密文特性的不同.这种利用物理混沌不可预测性的混合加密系统,不存在确定的明文密文映射关系,而且密文统计特性也应优于(或大致相当)其他加密系统.数值结果支持这一结论,同时表明不可预测性较强的混沌系统其加密产生的密文相关性较弱.     

Optics based biohashing using joint transform correlator

Most authentication process uses password and personal identification numbers (PIN) for security purposes. In order to remove the problem of hacking or stealing of the password and PIN numbers, there has been an increased interest in the utilization of specific biometric feature of the user. Recently, biohashing systems have been introduced for automatic biometric recognition. In a biohashing system, biohash codes are generated using the feature of the biometric. A basic biohashing system involves two steps. First is the extraction of the feature from the input biometric image and second is the discretisation of the obtained feature vector by using ortho-normalized random numbers. In this paper, a new biohashing system has been proposed in which joint transform correlator (JTC) has been used for extraction of the specific feature of the biometric. In the enrolment process, a biohash code has been generated by using a single face image and then stored. In the verification process, this biohash code is matched with the verification codes for recognition purpose. The main advantage of the proposed biohashing method is the possibility of the optical implementation of the feature extraction of the face image. Experimental as well as simulation results have been given to validate the proposed technique. Normalized Hamming distance has been calculated to discriminate the genuine and impostor face images. By varying the dimension of the feature matrix, the study of the variation of the normalized Hamming distance with the density of the population has been undertaken. For the performance evaluation of the proposed technique the false rejection ratio (FRR) and false acceptance ratio (FAR) have also been calculated.     

基于运动补偿和码率预分配的干涉多光谱图像压缩算法

提出了一种基于运动补偿的三维小波变换和基于码块预测的码率预分配的图像压缩算法.利用干涉多光谱图像成像推扫平移特性,在小波变换中使用运动补偿来减少帧间相关性,并对图像组中各个图像小波变换和量化后EBCOT编码码块的有效比特平面进行独立的熵估计.以图像估计熵总和指导整个图像组码率预分配,以解决平均分配码率对重建图像质量带来的影响.实验结果表明:该算法在8倍压缩时,图像序列的平均峰值信噪比比3D-SPIHT提高了0.85～1.25 dB,比单帧JPEG2000提高了1.91～4.25 dB, 算法复杂度低,易于硬件实现.     

基于复振幅场信息复用和RSA算法的非对称多幅图像认证方法

结合相位恢复和像素行、列循环移动置乱技术, 本文提出了一种基于复振幅场信息复用和RSA算法的非对称多幅图像认证方法, 通过菲涅耳域的相位恢复算法, 依次恢复并生成多幅图像各自所对应的输入平面的复振幅信息, 通过各自的行、列向量随机数对原始二值振幅模板进行行、列循环移动置乱操作来获得每幅图像的采样模板, 认证系统将多个复振幅场信息采样、叠加并空间复用, 同时, 行向量随机数和列向量随机数被RSA算法公钥编码成密文. 系统认证时, 认证方利用自己持有的私钥将密文解码成行向量随机数和列向量随机数, 通过行、列循环移动置乱变换后获得各自的采样模板, 合成的复振幅信息和采样模板等认证信息均放置在各自正确位置, 当认证系统被正确波长的平面波照射时, 在输出平面能获得输出图像, 通过计算、显示输出图像和对应认证图像的非线性相关系数峰值来判断认证是否成功.     

Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.     

A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.     

结合Lab空间和单尺度Retinex的自适应图像去雾算法

针对暗通道先验算法在大片天空区域透射率估计过小及景深突变处出现Halo效应的问题，提出一种结合Lab空间和单尺度Retinex的图像去雾算法。将RGB图像转换至Lab空间提取出亮度分量，利用Canny算子对亮度分量提取边缘信息，丰富恢复图像细节；利用单尺度Retinex对非边缘区域进行高斯自适应滤波估计出优化后的亮度分量，获得“伪”去雾图像，得到粗略的透射率；利用交叉双边滤波优化透射率消除Halo效应；最后根据大气散射模型恢复出无雾图像。实验结果表明，该方法恢复出的图像细节明显，整体平滑，且对含大片天空区域的图像也有较好的恢复效果。     

基于互补型光栅编码的相位展开

为了解决在运用相移技术和二元光栅编码结构光进行相位展丌时存在的误码问题,提出了一种新的互补型二元光栅编码.通过多投一幅格雷码图案,并利用格雷码相邻码字之间Hamming距离为1的特点,没计出互补的两种编解码方式.利用两种编码出现误码位置的不同,并结合相移技术解决了去包裹过程中的误码问题.论述了互补型二元光栅编码的设计方法及特点,并详细分析了基于互补型二元光栅编码的相位展开过程.计算机模拟和三维重建实验清楚地表明,提出的互补型光栅编码能很好地解决了误码问题且具有很高的可靠性.     

A block-based watermarking scheme for image tamper detection and self-recovery

In this paper, we present an effective block-based digital fragile watermarking scheme for image tamper detection and recovery. The proposed scheme embeds watermarks consisting of the authentication data and the recovery data into image blocks. It adopts parity check and the intensity-relation check to thwart various malicious attacks. In the tamper detection process, instead of independently testing the embedded authentication data of each block, we take the block-neighbourhood into account and utilize a hierarchical structure to determine the legitimacy of image blocks. Experimental results show that the proposed scheme can effectively resist collage attack, vector quantization (VQ) attack and constant-average attack, while sustaining superior accuracy of tamper localization. Furthermore, the results reveal that the tampered images can be successfully self-recovered with acceptable visual quality.     

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.