基于小波变换的图像置乱算法研究

随着计算机技术的快速发展,传统的图像置乱算法已经无法在满足人们的需求,通过人们不断的钻研,研究出了结合小波理论的图像置乱算法。本文针对小波变换的图像置乱加密算法进性了阐述,并且通过实验对其进行了验证,实验结果表明其效果良好。     

基于复合混沌模型的高级加密标准图像加密算法

为提高图像加密算法的安全性能,提出了一种基于复合混沌系统的改进的高级加密标准加密算法。该算法将Logistic混沌序列与Chebyshev混沌序列相结合,产生2种新的混沌映射。一种复合混沌矩阵作为高级加密标准的轮密匙,进行像素置乱;另一种复合混沌矩阵组成S盒,进行位置置乱。另外,对图像像素点进行位移及列混合操作,进一步达到位置置乱的目的,实现动态变换密钥的高级加密标准图像的加密。实验结果表明,该算法具有较高的密钥敏感性、良好的置乱效果及高效性。     

基于新型多环多翼超混沌系统的图像加密算法

目的 为提高图像加密算法的安全性,提出一种新型多环多翼超混沌系统的图像加密算法.方法 对超Lorenz混沌系统进行分形变换,得到多环多翼超混沌系统,设计一种基于该混沌系统的查表运算扩散算法.算法采用典型的置乱-扩散模式,借助Logistic混沌系统对明文图像进行无重复索引置乱,将新型多环多翼超混沌系统产生的密钥序列结合明文信息,对一次扩散图像的每个像素值进行正逆向扩散,得到最终的加密图像.结果 置乱图像的平均不动点比为0.003％,密文直方图分布均匀,相邻像素相关性几乎为零,信息熵达到了7.999,且NPCR,UACI和BACI的值分别为99.61％,33.46％和26.7％,与同类加密算法相比,该算法加密效果更优.结论 算法具有较强的密钥敏感性,能有效抵抗剪切、噪声等攻击,所提出的图像加密算法具有较高的安全性.     

基于改进广义Arnold映射的多混沌图像加密算法

目的针对Arnold映射图像置乱效果差以及无法抵御选择明文攻击等问题,提出一种改进广义Arnold映射的图像加密算法,以提高密文图像的安全性。方法该算法引入Kent映射产生控制参数,先对图像进行分块来削弱图像的相关性,对每块图像进行Arnold映射位置置乱,然后利用二维logistic映射生成混沌序列,经双曲正切变换处理后按照奇偶序列与置乱图像进行异或运算,得到最终密文图像。结果该算法得到密图信息熵为7.9899,像素改变率为99.61%,统一平均变化程度为30.73%。结论该算法简单易执行,能有效抵御各种统计攻击和明文攻击,具有密钥空间大、置乱效果好、鲁棒性较强等特点。     

基于混沌和置乱双重加密的数字图像水印算法

提出一种基于离散平稳小波变换的变换域的数字图像水印嵌入和提取算法.本算法先将数字水印图像进行置乱变换(Arnold变换),然后将置乱后的水印图像进行混沌加密,将加密后的水印按行展开成一维行向量,并将像素值从大到小排序,将原始图像平稳小波分解得到的低频系数也按行展开成一维行向量 ,并按从大到小排序.将排序后的水印图像嵌入排序后的低频系数中,变回二维后结合其它频域重构出水印图像 ,水印提取是嵌入过程的逆过程,将提取出的水印图像进行混沌解密、Arnold反置乱变换得到原始水印图像.本算法选择了合适的水印嵌入位置,实验结果表明:该算法使图像的不可见性好,而且所嵌入的水印对一般的图像处理攻击如噪声、滤波、旋转、压缩等有较强的鲁棒性.     

小波域鲁棒自适应灰度水印盲算法

提出了一个有效的基于图像特征的小波域灰度水印盲算法。该算法使用灰度图像作为水印,将水印信息进行 Arnold 置乱加密和小波压缩后,利用相邻特征平均值和奇偶判决法嵌入载体图像的二阶小波变换域中的低频子带上。实验结果表明,该算法具有好的透明性和强的鲁棒性。     

数字水印图像的置乱加密预处理研究

分析了分组置乱加密与混沌置乱加密算法,阐明了置乱加密预处理的安全性和置乱效果,提出了一种基于水印图像置乱加密的抗JPEG压缩的半脆弱数字水印算法.     

基于Curvelet变换的多聚焦图像融合方法

由于可见光成像系统的聚焦范围有限,很难获得同一场景内所有物体都清晰的图像.多聚焦图像融合技术可有效地解决这一问题.在分析了传统多聚焦图像融合方法和Curvelet变换的原理后,提出了一种基于Curvelet变换的多聚焦图像融合方法,先对不同聚焦图像分别进行Curvelet变换,采用低频系数取平均,高频系数取大的融合规则,再进行Curvelet反变换得到融合结果.仿真试验表明,基于Curvelet变换的融合方法可有效综合多聚焦图像,相比小波变换法,获得了更好的融合效果.     

基于超混沌的三层量子图像加密算法研究

具有高效运算能力的量子计算机的提出使得基于经典计算机的图像加密算法安全性大大降低,如何将图像转移到量子计算机表示并进行加密传输是一个难点。针对这一问题,设计了基于NEQR模型的3层量子图像加密算法。首先,利用希尔伯特(Hilbert)矩阵实现量子块级置乱;然后,利用超Lorenz混沌系统生成的随机序列,打乱量子像素中的位顺序,实现位级置乱;最后,利用量子随机图像对原始量子图像进行CNOT操作并进行位本身CNOT操作,实现位级扩散,得到最后的加密图像。实验结果表明,加密图像的相关系数较低,加密算法的密钥空间大,加密图像的像素改变率、归一化平均变化强度和熵值接近理论值,具有良好的安全性和可行性。     

基于多小波变换和分块SVD的彩色图像水印算法

提出了一种基于多小波变换和分块SVD的非盲水印算法。选择彩色图像的饱和度分量作为水印的嵌入域,水印为有意义的灰度图像,水印经Arnold置乱后被嵌入到饱和度分量多小波变换的不同中频区域中。实验证明,该算法不仅对噪声、JPEG压缩、剪切及Photoshop处理具有很好的鲁棒性,而且能抵抗旋转、放缩和平移等几何攻击。     

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.     

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image. Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones, and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.     

基于自制标尺的C臂X光全景图像拼接方法

在对C臂X光图像和现有的医学图像匹配方法分析的基础上,提出了一种基于自制标尺的C臂X光图像拼接方法.该图像拼接方法分为失真校正、图像匹配和图像合成三步,其中图像匹配是算法的关键.首先采用双线性变换模型进行失真校正;然后基于自制标尺,采用图像灰度匹配法对校正后的C臂X光图像进行图像匹配;最后将其余各幅图像间的匹配矩阵均换算成以第一幅图像为基准,据此合成出全景图像.通过尸腿骨X光图像拼接实验验证了该方法的准确性.     

基于复杂网络的产品形态意象与要素挖掘方法

目的 在大数据时代背景下,为有效利用在线评论信息及准确地掌握用户对产品设计的感性意象需求,挖掘产品形态核心要素,提出1种基于复杂网络的产品形态意象与要素挖掘方法。方法 首先,爬取产品的在线评论信息,经语言处理获得候选感性意象节点集合,使用相关性计算构建出产品形态意象网络;然后,应用复杂网络中心性理论,对网络进行拓扑分析,计算度中心性、接近中心性、介数中心性和相应的特征权重,获得各意象节点的综合中心性特征值,经方差计算,获得节点的灵敏性特征值;接着,按意象Kano模型划分节点类型,经社团分析进行意象解耦,获得产品设计目标意象、应用意象熵、计算目标意象的综合值;最后,建立多意象加权驱动的产品形态要素挖掘方法,应用复杂网络及其分析方法获得核心要素,进而指导形态设计。结果 以口红形态为研究对象,运用基于复杂网络的产品形态意象与要素挖掘方法获得了“高端”“时尚”“精致”的目标意象,并计算得到综合意象,构建出口红形态要素网络,经分析获得了3个核心要素。结论 通过实例验证,该方法可获得更为客观、全面和准确的产品形态目标意象及核心要素,将有效指导意象形态创新设计。     

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user.     

复杂背景下导弹目标的分光检测

针对电视制导系统从各种复杂自然背景和野战环境的图像中提取导弹目标信号的要求,从导弹目标和背景的光谱特性出发,提出了导弹目标的分光检测原理与方法,设计了双CCD分光检测光学系统,使含有导弹目标的图像进入主CCD,背景图像进入次CDD,最终由背景减法电路实现主次CCD图像相减,得到导弹目标图像.实验结果证明,该方法能可靠地从背景中检测出导弹目标.     

A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content, original images are encrypted using block scrambling, pixel circular shift, and pixel diffusion when uploaded to the cloud server. It is proved that RILBP features remain unchanged before and after encryption. Moreover, the server can directly extract RILBP features from encrypted images. Analyses and experiments confirm that the proposed scheme is secure and effective, and outperforms previous secure LBP feature computing methods.     

DWT-based blind and robust watermarking using SPIHT algorithm with applications in tele-medicine

Malicious manipulation of digital images cannot be ignored in certain critical application like tele-medicine, where medical records of the patients (mostly in the form of diagnostic images of brain, chest, heart, lungs, etc.) need to be securely transmitted over the internet for various life-saving consultation and treatments. Watermarking is used to protect such documents from being manipulated and helps detect the authenticity of the image received. A robust and blind image watermarking technique based on discrete wavelet transform is proposed in this paper. For embedding purpose, suitable coefficients are found out using the Set Partitioning in Hierarchical Trees algorithm. Level of security is further enhanced by scrambling the watermark using Arnold transform and by making the set of embedding locations random using a secret key. Noise Visibility Function is used to compute the minimum allowable distortion for each coefficient before embedding to achieve better image fidelity. Experimental results show that the proposed scheme is robust against certain standard image processing attacks and outperforms the prior existing similar types of watermarking schemes.     

包装中专色印刷图像数字水印方法

目前,包装装潢图的印刷图像水印算法主要是针对传统的四色印刷方式,因而其用于专色印刷中效果不佳。基于包装中专色印刷和扫描图像特征,结合整数小波转换（IWT）和奇异值分解（SVD）提出了一种可用于专色图像印刷的鲁棒水印算法。先对专色通道进行IWT转换,然后将水印信息直接嵌入一级IWT各子带的奇异值S中;为了提高水印安全性,对水印图像进行了Arnold置乱加密,并在嵌入水印的图像中嵌入一个数字签名;提取水印时,基于专色图像印刷-扫描特征,将专色印刷图像扫描成灰度图,提取数字签名进行验证,验证通过后再提取水印信息。为了验证算法的有效性,选用胶版印刷方式对本文算法进行了一系列实验测试,并与文献[8]所提算法进行了比较,结果表明,本算法应用于专色印刷中具有很好的视觉不可见性,且可以抵抗胶版印刷过程。     

基于机器视觉的二维图像质量缺陷检测研究进展

目的 机器视觉图像处理技术是近年在图像处理领域发展起来的一门新兴边缘交叉学科，二维图像的质量检测是印刷行业中必不可少的环节，分析基于机器视觉的二维图像质量缺陷检测流程，探索影响基于机器视觉的二维图像质量缺陷检测精度的相关因素，为后续研究印刷品的二维图像自动化检测和质量控制提供参考。方法 在此基础上，围绕图像预处理中的灰度转换、噪声过滤、固定阈值分割、自适应阈值分割、Otsu法及边缘检测，对图像配准中的基于灰度统计信息分布配准方法、基于特征的图像配准方法进行总结，然后归纳分析图像的缺陷提取和分类。结论 以实际例子对上述研究内容进行了提炼，通过图像预处理中的噪声过滤为后续缺陷提取提供清晰图像，减少伪影干扰；通过图像预处理中的灰度变换、阈值分割、感兴趣区域提取减少系统处理时间，为实现高效的缺陷检测奠定了坚实的基础；通过图像配准消除了机械振动引起的图像位置偏移，确保后续缺陷提取的准确性；通过图像缺陷提取和分类帮助印刷企业找出生产问题，提供有针对性的改进措施，可为生产高质量产品提供支持。