对一种抗统计分析的LSB隐写方案的攻击

针对张新鹏等人提出的抗统计分析的LSB隐写方案对图像差分直方图的影响，本文提出了一种基于差分直方图分析的隐写分析方案．该方案使用隐写前后图像差分直方图转移矩阵结合对差分直方图广义拉普拉斯分布的拟合偏差实现对嵌入数据长度的估计．实验证明该算法具有较好的估计精度．     

图像隐写与自然噪声辨识研究

提出一种辨识图像隐写与自然噪声的方法。从图像中常见的2类噪声(高斯白噪声和椒盐噪声)出发,基于加性噪声模型,利用图像直方图特征函数质点区分原始图像和隐写噪声图像,利用小波高频子带系数差分方差识别隐写图像与噪声图像。对大量隐写和噪声图像进行实验,结果表明,该方法可有效辨识图像隐写和噪声。     

视频运动图像中人脸清晰度的分析与捕捉

图像的清晰度主要取决于它的高频分量的能量,离散快速傅里叶变换和小波变换,是两种优秀的频域变换工具。介绍了利用离散快速傅里叶变换和小波变换分析运动图像中人脸清晰度的方法：首先利用直方图均衡化以及形态学操作对图像进行预处理;然后进行人脸检测和人脸跟踪;最后进行人脸的捕捉。对比了离散快速傅里叶变换和几种小波变换的质量和效率,实验证明基于bior基的小波变换是最有效的。     

基于时空相关性的视频隐写分析

视频中的扩频嵌入是一种典型的隐写方法,能够抵抗视频压缩、噪声添加等多种攻击。为了有效检测利用该方法嵌入的隐藏信息,将隐写过程建模为加性高斯噪声的叠加过程,分析了噪声叠加对视频序列的时间相关性和空间相关性的影响,提出了基于时空相关性的隐写分析算法。该算法利用视频序列中各帧的四方向差分直方图度量隐写对空间相关性的影响,同时利用相邻帧帧间差分的直方图度量隐写对时间相关性的影响。与现有算法相比,本文算法实现简单,实验结果表明本文算法具有更好的检测性能。     

一种针对小波域隐匿图像的隐写分析方法

通过对图像小波域差分直方图的统计观察,提出一种针对小波域信息隐藏的隐写分析方法.该方法对图像进行小波分解,得到每个小波子图的水平方向系数差分直方图,通过对该差分直方图频谱的分析,发现小波域隐写图像的差分直方图频谱的分布规律与未隐写图像有明显差别,据此实现对小波域隐写图像的检测,并可估算隐写信息长度.实验结果表明该方法检测正确率高、检测速度快,实用性强.     

以直方图差值序列为特征的JPEG MB2隐写分析

基于JPEG图像的离散余弦变换(DCT)系数直方图差值序列,提出一种新的检测JPEG MB2隐写术的方法。该算法定义JPEG图像的DCT系数一阶直方图与其Cauchy拟合分布模型的系数直方图之间的差值序列作为相关性度量,在此基础上构造区分隐写图像和载体图像的分类特征序列。在特征分析中,通过Hilbert-Huang变换对特征序列进行经验模式分解,构建了基于Hilbert谱的特征向量。实验结果表明,基于该特征向量的支持向量机(SVM)对MB2隐写算法检测平均准确率达到84.48%,该方法也适用于JPEG MB1的检测。     

基于像素差分相关性的LSB匹配隐写分析

针对空间域LSB匹配隐写,提出了一种基于像素差分相关性的隐写分析方法.采用图像的直方图、差分直方图的平滑度、梯度能量、图像平滑度,像素差分的一维和高维统计分布等作为分类特征,结合Fisher线性判别进行隐写分析.实验结果表明,新算法对于LSB匹配的检测具有较高的准确性,且计算复杂度低.该算法还可应用于其他空间域隐写方法的检测.     

基于离散傅里叶不变特征的人脸识别

给出了一种基于离散傅里叶不变特征的人脸识别方法。从连续傅里叶变换出发,讨论连续傅里叶变换情况下的傅里叶变换性质,给出离散傅里叶变换情况下的傅里叶变换性质。依据离散傅里叶变换性质,推导出离散傅里叶变换的不变特征,并将其用于人脸图像识别。人脸识别结果表明方法具有很好的识别能力。     

基于直方图特性的LSB匹配隐写分析研究

为了提高对高精度原始图像LSB匹配数字隐写的检测能力,提出了基于直方图特性的LSB匹配隐写分析方法.根据高精度原始图像在LSB匹配数字隐写过程前后灰度直方图、差分直方图以及小波分解后的子带系数直方图的特性差异,提取了23维的特征向量,并使用支持向量机对其进行训练,建立区分载体图像与载密图像的分类器达到检测秘密信息的目的.实验结果表明,该方法在性能上要优于文献[5]的局部极值法.     

基于LSBMR图像边缘自适应隐写的检测

在图像边缘自适应LSB匹配改进隐写算法中,秘密信息嵌入位置的选择仅由某个方向上像素对的差值决定,未考虑该像素与其邻域内其他像素的差值变化的特点.针对该问题,对隐写前后图像的八方向差分直方图进行分析,提出一种基于LSB匹配改进算法(LSBMR)边缘自适应隐写检测的算法.该算法计算图像的八方向绝对差分直方图,提取直方图中隐写前后变化较为明显的频数用以构建特征向量,并使用支持向量机完成检测.对较低嵌入率下(≤0.5 bpp)的EALSBMR隐写结果进行检测,结果表明该算法的平均检测率均高于现有典型的隐写分析算法.     

Optimized Feature Extraction for Learning-Based Image Steganalysis

The purpose of image steganalysis is to detect the presence of hidden messages in cover photographic images. Supervised learning is an effective and universal approach to cope with the twin difficulties of unknown image statistics and unknown steganographic codes. A crucial part of the learning process is the selection of low-dimensional informative features. We investigate this problem from three angles and propose a three-level optimization of the classifier. First, we select a subband image representation that provides better discrimination ability than a conventional wavelet transform. Second, we analyze two types of features-empirical moments of probability density functions (PDFs) and empirical moments of characteristic functions of the PDFs-and compare their merits. Third, we address the problem of feature dimensionality reduction, which strongly impacts classification accuracy. Experiments show that our method outperforms previous steganalysis methods. For instance, when the probability of false alarm is fixed at 1%, the stegoimage detection probability of our algorithm exceeds that of its closest competitor by at least 15% and up to 50%     

Reversible Steganography for VQ-Compressed Images Using Side Matching and Relocation

The reversible steganographic method allows an original image to be completely reconstructed from the stegoimage after the extraction of the embedded data. The traditional reversible embedding schemes are not suitable for images compressed using vector quantization (VQ) and usually require the use of the location map for reversibility. In this paper, we propose a reversible embedding scheme for VQ-compressed images that is based on side matching and relocation. The new method achieves reversibility without using the location map. The experimental results show that the proposed method is practical for VQ-compressed images and provides high image quality and embedding capacity     

一种自适应的DFT域数字水印算法

论文提出一种用于数字图像版权保护的DFT域水印算法。该算法对图像分块进行傅立叶变换,利用图像的统计特性和人眼对图像变化的敏感性、自适应地选取相应的图像块及图像块中幅度谱系数的重要性来嵌入水印。该算法在提取水印时不需要原始图像。实验证明,此算法简单易行,具有很好的不觉察性,而且对JPEG压缩、图像的尺度变换、旋转攻击、中值滤波以及噪声干扰等都具有很强的鲁棒性。     

Histogram-based reversible data hiding for vector quantisation-compressed images

Reversible data hiding is required and preferable in many applications such as medical diagnosis, military, law enforcement, fine art work and so on. The author proposes to use reversible data hiding applications with a vector quantisation (VQ)-compressed image. The histogram of the prediction VQcompressed image is explored. The prediction VQ encoded image is identical to traditional VQ encoding. The index of prediction encoded VQ images is modified to embed secret data. Furthermore, the VQ images can be completely reconstructed by the recovery procedure. The experimental results show the performance of the proposed method and the efficiency of the embedding, extraction and recovery procedures. In comparison with other VQ-based schemes, the proposed method provides a higher hiding capacity and a better stegoimage quality. Also, the lossless VQ image is recovered     

A robust content based image watermarking using local invariant histogram

Desynchronization attack is known as one of the most difficult attacks to resist, which can desynchronize the location of the watermark and hence causes incorrect watermark detection. Based on multi-scale SIFT (Scale Invariant Feature Transform) detector and local image histogram shape invariance, we propose a new content based image watermarking algorithm with good visual quality and reasonable resistance toward desynchronization attacks in this paper. Firstly, the stable image feature points are extracted from the original host by using multi-scale SIFT detector, and the local feature regions (LFRs) are constructed adaptively according to the feature scale theory. Then, the discrete Fourier transform (DFT) is performed on the LFR, and the local image histogram is extracted from a selected DFT amplitude range. Finally, the bins of the histogram are divided into many groups, and the digital watermark is embedded into LFR by reassigning the number of DFT amplitudes in bin groups. By binding the watermark with the geometrically invariant image features, the watermark detection can be done without synchronization error. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as sharpening, noise adding, and JPEG compression, but also robust against the desynchronization attacks such as rotation, translation, scaling, row or column removal, and cropping.     

Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

In this paper, we present a new approach for fingerprint classification based on discrete Fourier transform (DFT) and nonlinear discriminant analysis. Utilizing the DFT and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low-quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.     

多重秘密共享的DCT域二值图像隐藏方案

研究了一种新的二值图像隐藏方案,即将一幅二值图像隐藏在多幅载体图像中。针对二值图像取值形式为 二值的特点,该方案第一次把变长游程编码算法、多重秘密共享思想和IX了1域的信息隐藏算法结合在一起,并根据载 体图像的最大隐藏信息量把秘密图像游程长度序列分成n块,由n个密钥控制并分别隐藏在n幅载体图像中。实验 表明,该算法在不明显引入修改痕迹的情况下,可显著提高安全性和鲁棒性,并且可以无损还原。     

Curvelet transform and cover selection for secure steganography

In this paper, we present curvelet transform (CT) based image steganography that embeds scrambled secret image in appropriately selected cover image. Curvelet transform offers optimal nonadaptive sparse representation of objects with edges and possesses high directional sensitivity and anisotropy. Cover image is decomposed using curvelet transform and adaptive block based embedding is carried out only in non-uniform regions of high frequency curvelet coefficients. In addition, this work also demonstrates a new cover selection method to choose suitable cover from image database. Spatial information based image complexity is modelled using fuzzy logic to identify set of images that yields least detectable stego image. From this set of ranked images, best cover can be chosen for carrying secret information depending on amount of information to be embedded. Cover selection offers reduced risk of detectability and ensures security. It is evident from experimental results that proposed method outperforms conventional methods in terms of imperceptibility, robustness and security.     

采用DFT域嵌入的全息水印方法

研究了基于计算数字全息的数字水印技术,针对水印的稳健性与不可见性的矛盾,提出了一种基于离散傅立叶变换域嵌入二值全息图的数字水印算法,并进行了计算机仿真实验.本算法首先生成二值水印图像的傅立叶全息图并进行二值化,再对载体图像进行傅立叶变换,将二值化的全息图嵌入到载体图像傅立叶变换幅度谱中.使用该方法嵌入的水印具有良好的不...     

基于风格迁移纹理合成与识别的构造式信息隐藏

传统的信息隐藏算法大都通过修改载体达到隐藏秘密信息的目的,但不可避免地会在载体数据中留下修改痕迹,故常难以抵抗隐写分析技术的检测,为此无载体信息隐藏应运而生.无载体信息隐藏并非不使用载体,而是不对载体数据进行修改.为了提高无载体信息隐藏算法的隐藏容量和鲁棒性,提出了一种基于风格迁移纹理合成与识别的构造式信息隐藏算法.该算法首先选取不同类别的自然图像和纹理图像分别建立内容图像库和纹理风格图像库,并根据内容图像库中自然图像的类别构建二进制码的映射字典;其次为了接收方能够从含密图像中提取出秘密信息,需要构建带标签的纹理图像库,并将其作为训练集输入到卷积神经网络中,通过迭代训练获得纹理图像识别模型.在秘密信息隐藏时,根据秘密信息片段选择对应类别的自然图像,并按照一定的顺序组合成含密拼接图像,随后从纹理图像库中随机选择一张纹理图像,通过风格迁移的方法将含密拼接图像转换成含密纹理图像,从而完成秘密信息隐藏过程.在信息提取过程中,通过纹理图像识别模型可准确识别出含密纹理图像原本对应的图像类别,再对照映射字典即可提取出秘密信息.实验结果表明,所提算法生成的含密纹理图像具有良好的视觉效果,秘密信息隐藏容...