基于神经网络的隐写图像盲检测系统设计

将秘密信息隐藏在图像中通过网络传输是当前一种十分流行的隐蔽通信手段。针对隐写图像的检测国际上已经公布了许多检测算法。本文在分析这些算法局限性的基础上利用BP神经网络方法来设计图像盲检测系统。其目的是在没有原图像载体的条件下提高发现网络中的隐写图像的准确率。最后本文简要给出了基于神经网络的隐写图像设计要点、实现框图和优缺点。     

基于图像的广义隐写分析

主要对基于图像的广义隐写分析算法进行了研究,选取4种对信息隐藏分析效果最好的图像质量度量标准(Image Quality Metrics),运用误差反传(Back Promulgate)神经网络设计了一套广义隐写分析系统,并将其用于最终隐写图像的检测分类上.经实验证明,该方法具有较高的准确率和普遍的适应性,因而可用于实际的隐写图像检测分析中.     

基于小波分解和方差分析的图像信息隐藏盲检测

为了实现图像中隐藏信息的盲检测,建立高阶统计模型,提取高阶小波统计量捕获原始图像和隐藏图像之间的统计差异;方差分析用于检验所提取的小波统计量对隐藏信息的敏感程度。应用方差分析选取出对隐藏信息较敏感的小波统计量作为图像的特征向量元素,基于核技巧的支撑向量机(SVM)用作原始图像与隐藏图像之间的分类器,实现图像中隐藏信息的盲检测分析。实验结果及分析表明本文的方法能较有效地实现信息隐藏的盲检测分析。     

基于小波系数相关性的空域隐写分析方法

基于小波系数相关性,提出了一类具有较高正确检测率的空域隐写通用型检测方法。首先利用互信息分析秘密信息嵌入对图像小波系数在尺度方向和空间方向相关性的影响,并使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征;然后对提取的特征进行加权融合并结合Fisher线性判别(FLD)分类器进行分类。针对LSB(least significant bit)、LSBmatching和SM(stochastic modulation)隐写算法的实验表明,在不增加计算复杂度的情况下,本文方法相比现有的典型空域隐写通用型检测方法,正确检测率有明显提高。     

基于小波包分解的船舶发电机轴承故障检测

为了实现对船舶发电机轴承进行故障检测,通过对三相定子电流的Park失量模信号进行小波包分解,求相应子频带小波包分解的均方根值(RMS)作为表征电机轴承的故障特征,以此作为发电机轴承故障诊断的依据。在实验室条件下,选择一款发电机并人为设置轴承故障进行试验验证,实验结果表明,提出的方法能够有效的识别发电机的轴承故障。     

基于小波包分解的图像特征提取及其应用

提出了一种基于小波包分解的提取图像特征的算法,将图像在不同尺度下的小波包变换模极大值组成一个矩阵,采用奇异值分解得到该矩阵的奇异值矢量作为描述信号调制样式的特征向量。该算法克服了传统的通过各种频域变换提取图像特征时对图像特征难以充分描述的不足,提取的特征向量的维数相对不高,便于实现。计算机仿真结果表明,这种方法具有稳健的抗噪性和良好的扩展性。     

基于小波包分解的导弹目标神经网络识别

提出了基于小波包分解特征的神经网络导弹目标识别算法。在该算法中,首先通过小波包分解获取能够反映时间序列时频信息的稳定特征,然后利用训练过的神经网络提取特征对导弹目标进行识别。文中运用一组仿真数据和一组试验数据对该算法进行测试,结果表明该算法具有较高的识别概率。     

基于小波包分解的信号分解与重构研究

为有效剔除信号噪声,得到没有污染的信号,便于进行信号分析。文章对小波包信号分解原理进行了阐述,并运用matlab仿真了信号的分解与重构,并展示了分解示意图和重构结果,较好的验证了算法的实用性。     

信息隐写与隐写分析研究框架探讨

文章分析了广义信息隐藏研究中存在的问题，提出引入全信息理论和模糊信息处理的思想进行研究-对信息空间从三个层面上进行了分类，将隐写和隐写分析问题放在信息空间中进行研究，建立了隐写分析研究框架.     

一种基于游程长度和隐写分析特征融合的图像拼接盲检测方法

提出了一种基于游程长度(RLRN)和隐写分析特征融 合的图像拼接检测算法。算法中的隐写分析特征是在 图像经分块离散余弦变换(DCT)后的系数矩阵中提取,并将其和RLRN特征进行融 合。特征提取在 色度(chroma)空间进行,用支持向量机(SVM)作为分类器。实验结果显示,融合后的特征在 图像测试库CASIA v1.0 和CASIA v2.0上识别率分别达到98.57%和97.27%,不仅比特征在融合前的识别率有较大提高,而且和现 有的一些算法相比,提出的特征融合算法也具有良好的识别性能。     

一种针对JPEG图像的通用隐写分析算法

提出了一种针对JPEG图像的通用隐写分析算法。该算法提取了15个具有良好分类特性的特征参数,输入构建的KS—SVM分类器．以达到检测载密图像的目的。实验结果表明,该算法的检测正确率较高,检测速度快,能够实现针对各类JPEG载密图像的有效检测。     

A blind audio steganalysis based on feature fusion

In this paper, we present a blind steganalysis based on feature fusion. Features based on Short Time Fourier Transform (STFT), which consists of second-order derivative spectrum features of audio and Mel-frequency cepstrum coefficients, audio quality metrics and features on linear prediction residue are extracted separately. Then feature fusion is conducted. The performance of the proposed steganalysis is evaluated against 4 steganographic schemes: Direct Sequence Spread Spectrum (DSSS), Quantization Index Modulation (QIM), ECHO embedding (ECHO), and Least Significant Bit embedding (LSB). Experiment results show that the classifying performance of the proposed detector is much superior to the previous work. Even more exciting is that the proposed methodology could detect the four steganography, with 85%+ classification accuracy achieved in all the detections, which makes the proposed steganalysis methodology capable of being regarded as a blind steganalysis, and especially useful when the steganalyzer are without the knowledge of the steganographic scheme employed in data embedding.     

Unbalanced JPEG image steganalysis via multiview data match

Image steganalysis must address the matter of learning from unbalanced training sets where the cover objects (normal images) always greatly outnumber the stego ones. But the research in unbalanced image steganalysis is seldom seen. This work just focuses on the problem of unbalance JPEG images steganalysis. In this paper, we propose a frame of feature dimension reduction based semi-supervised learning for high-dimensional unbalanced JPEG image steganalysis. Our method uses standard steganalysis features, and selects the confident stego images from the unlabeled examples by multiview match resampling method to rebalance the unbalanced training images. Furthermore, weighted Fisher linear discriminant (WFLD) is proposed to find the proper feature subspace where K-means provides the weight factor for WFLD in return. Finally, WFLD and K-means both work in an iterative fashion until convergence. Experimental results on the MBs and nsF5 steganographic methods show the usefulness of the developed scheme over current popular feature spaces.     

Block-based image steganalysis: Algorithm and performance evaluation

Traditional image steganalysis is conducted with respect to the entire image frame. In this work, we differentiate a stego image from its cover image based on steganalysis of decomposed image blocks. After image decomposition into smaller blocks, we classify image blocks into multiple classes and find a classifier for each class. Then, steganalysis of the whole image can be obtained by integrating results of all image blocks via decision fusion. Extensive performance evaluation of block-based image steganalysis is conducted. For a given test image, there exists a trade-off between the block size and the block number. We propose to use overlapping blocks to improve the steganalysis performance. Additional performance improvement can be achieved using different decision fusion schemes and different classifiers. Besides the block-decomposition framework, we point out that the choice of a proper classifier plays an important role in improving detection accuracy, and show that both the logistic classifier and the Fisher linear discriminant classifier outperforms the linear Bayes classifier by a significant margin.     

Effects and solutions of Cover-Source Mismatch in image steganalysis

The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific reference database, namely BOSSbase, recent works show that, because of CSM, the results may greatly differ when changing this dataset. Although the CSM has already been the subject of several publications, these prior works investigated only a few elements in a limited setup. The goal of the current paper is to study the effects of the CSM in a more comprehensive manner and then to examine and compare different strategies for mitigating it. It first defines two different parameters, the source difficulty and the source inconsistency, which are involved in the CSM. Then, using different steganographic schemes and feature sets, it aims at providing a systematic study regarding the various factors that can give birth to CSM for image steganalysis. Finally, two practical ways to mitigate the CSM, using training techniques promoting either diversity of different sources or the specificity of one targeted source which is beforehand identified by training a multi-class classifier, are presented and their performances are compared for different training set sizes.     

基于特征融合稀疏表示的JPEG隐写分析

提出一种新的JPEG图像隐写分析方法,即基于特征融合的稀疏表示隐写分析方法。首先介绍所选特征的提取方法并分析所选特征之间的互补性与冗余性,然后利用主成分分析方法将所选特征降维进行融合,最后在此特征上利用向量总变差进行稀疏求解,用稀疏表示进行隐写检测。理论分析和实验表明该方法比单一特征的稀疏表示具有更高的识别率。     

Binary image steganalysis based on pixel mesh Markov transition matrix

Binary image stego systems have already been well developed, which raises the requirement of a steganalytic method that detects these stego systems reliably. In this paper, a steganalytic method based on the pixel mesh Markov transition matrix (PMMTM) is presented to detect binary image steganography in the spatial domain. The proposed scheme measures the embedding distortion on the texture consistency. Further, the dependence among texture structures is organized as the Markov transition of pixel meshes. The final dimensionality-reduced feature set is formed by shrinking the obtained PMMTM according to its detection performance on the embedding simulators, which are developed to simulate practical stego systems. In the end, experimental results are reported, demonstrating that the proposed approach can effectively and reliably detect state-of-the-art binary image stego systems.     

Multichannel blind iterative image restoration

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.