基于模糊C均值聚类与单类支持向量机的音频隐写分析方法

针对传统的二分类音频隐写分析方法对未知隐写方法的适应性较差的问题,提出了一种基于模糊C均值(FCM)聚类与单类支持向量机(OC-SVM)的音频隐写分析方法。在训练过程中,首先对训练音频进行特征提取,包括短时傅里叶变换(STFT)频谱的统计特征和基于音频质量测度的特征,然后对所提取的特征进行FCM聚类得到C个聚类,最后送入多个超球面的OC-SVM分类器进行训练;检测过程中,对测试音频进行特征提取,根据多个超球面OC-SVM分类器的边界对待测音频进行检测。实验结果表明,该隐写分析方法对于几种典型的音频隐写方法能够较为正确地检测,满容量嵌入时,测试音频的总体检测率达到85.1%,与K-means聚类方法相比,所提方法的检测正确率提高了至少2%。该隐写分析方法比二分类的隐写分析方法更具有通用性,更适用于隐写方法事先未知情况下的隐写音频的检测。     

基于KFD指标聚类的高隐蔽性JPEG隐写分析

采用非公开的图像源或算法的隐写行为具有很强的隐蔽性.在这类对隐写者先验不足的场景下聚类分析更为实用.Ker等人比较不同指标不同配置之后,提出基于MMD指标聚类的隐写者识别方法.然而该方法所用MMD指标只考虑两个类样本中心之间的距离,忽略了样本相对中心点的聚合程度对可分性的影响,因而准确率存在提高的空间.为进一步提高现有隐写聚类分析方法的准确率,该文提出用核Fisher鉴别(KFD)指标计算样本间差异度量的聚类方法.首先,提取PEV274校准特征并归一化.然后,计算KFD指标组成距离矩阵.最后,根据样本间差异度量矩阵按重心法自底向上进行层次聚类分析.KFD指标兼顾与最大平均距离(MMD)原理相近的类间方差以及指示样本聚集程度的类内方差,更准确地估算样本间差异.实验结果表明,该文对低嵌入率隐写其准确率最高提高约30％,对高嵌入率准确率降低不超过5％.该文的创新点在于提出了一种更合理的指标和基于该指标聚类隐写分析的方法,比现有方法平均准确率有一定的提高.     

分类与分割相结合的JPEG图像隐写分析

目的 隐写分析研究现状表明,与秘密信息的嵌入过程相比,图像内容和统计特性差异对隐写检测特征分布会造成更大的影响,这导致图像隐写分析成为了一个"相同类内特征分布分散、不同类间特征混淆严重"的分类问题。针对此问题,提出了一种更加有效的JPEG图像隐写检测模型。方法 通过对隐写检测常用的分类器进行分析,从降低隐写检测特征类内离散度的角度入手,将基于图像内容复杂度的预分类和图像分割相结合,根据图像内容复杂度对图像进行分类、分割,然后分别对每一类子图像提取高维富模型隐写检测特征,构建分类器进行训练和测试,并通过加权融合得到最终的检测结果。结果 在实验部分,对具有代表性的隐写检测特征集提取了两类可分性判据,对本文算法的各类别、区域所提取特征的可分性均得到明显提高,证明了模型的有效性。同时在训练、测试图像库匹配和不匹配的情况下,对算法进行了二分类测试,并与其他算法进行了性能比较,本文算法的检测性能均有所提高,性能提升最高接近10%。结论 本文算法能够有效提高隐写检测性能,尤其是在训练、测试图像库统计特性不匹配的情况下,本文算法性能提升更加明显,更适合于实际复杂网络下的应用。     

基于多特征的空域替换类图像隐写检测方法

提出一种基于多特征的空域替换类图像隐写检测方法。通过分析空域替换隐写原理,找出空域替换类隐写的共性,分别用位平面纹理相似性和位平面统计相关性以及位平面低比特位随机性来度量和刻画该类隐写对图像位平面问关系属性以及图像位平面属性的影响,由此构建针对空域替换类的隐写检测方法。实验结果表明该方法有效,并具有较高的检测准确性和较强的适应性。     

一种设计层次支持向量机多类分类器的新方法

层次结构的设计是层次支持向量机多类分类方法应用中的关键问题,类间可分性是设计层次结构的重要依据,提出了一种基于线性支持向量机度量类间相似程度的方法,并给出了一种基于类间可分性设计层次支持向量机多类分类器的新方法。实验表明,新方法有效地提高了层次支持向量机多类分类器的分类精度和速度。     

分级安全的文本隐写方法

针对单一数据类型隐写方法安全性不高、隐写容量不足等问题,提出了一种具有分级安全的文本隐写方法。首先,将整个载体文档中的多种类型的数据作为备选隐写载体,以不同类型数据的隐写特点和隐写分析技术为评估依据定义隐写安全等级,构建了一个多类数据融合的分级安全隐写模型。然后,根据秘密信息的长度自适应确定安全等级,并利用分级安全隐写模型将秘密信息分块地嵌入在同一个载体文档相互独立的多个不同类数据中。理论分析及实验结果表明,与现有基于单一数据类型隐写方法相比,所提方法扩大了隐写容量,在嵌入等量的秘密信息情况下,降低了文档中同一类载体数据的统计特征改变程度,提高了秘密信息的整体安全性。     

基于类间可分性度量的支持向量机决策树

采用支持向量机解决多类分类问题一般通过多个两类分类器的组合来求解,如何组合这些两类分类器就是该方法的关键.提出一种改进的支持向量机决策树多类分类模型,该模型通过引入类间可分性度量来确定决策树结构,以类间可分性度量的高低来决定不同类别在决策树中的位置,将容易分离的类尽可能早地划分出来.最后通过一组实验证明了该模型的有效性.     

基于改变率自适应分类的多类隐写分析

一般的多类隐写分析需将每种隐写算法的各种嵌入率当作一类进行训练,因其在构造分类器时未能充分考虑算法和嵌入率对分析能力的影响,故而准确率存在一定的提升空间。提出一种基于改变率自适应分类的多类隐写分析方法,将隐写改变率和算法差异性两方面因素分层考虑。该方法使用支持向量回归法估计待测图像的改变率,进而根据改变率自适应地选择分类器,从而提高分类准确率。实验结果表明,所提方法相较于现有准确率最高的方法准确率平均提高约2%～3%,特别在嵌入率较低的情况下,提高幅度可达5%以上。     

面向硬件木马检测的旁路信号特征选择方法

针对基于旁路分析的硬件木马检测中存在的旁路信号冗余以及高维问题,探究特征选择方法在去除冗余、降低旁路信号维数方面的可行性,提出了一种以类内类间距离作为可分性判据的特征选择方法对旁路信号进行预先处理。首先分析了IC芯片旁路信号的特征选择问题,然后阐述了基于类内类间距离的可分性判据以及特征选择搜索算法,最后在FPGA密码芯片中植入硬件木马,并基于K-L方法进行检测实验,通过对旁路信号进行特征选择前后的木马检测效果对比发现,该特征选择方法能有助于分辨出无木马的“金片”与含木马芯片之间旁路信号的统计特征差异,更好地实现硬件木马的检测。     

基于特征融合聚类的JPEG盲隐写分析

传统隐写分析所需的隐写算法、嵌入率和图像来源等先验知识在实用中很难满足,上述条件未知的盲隐写分析场景下,使用聚类分析方法可以有效区分隐写者与非隐写者。设计一种适合所选特征的融合方案,用以提高JPEG聚类隐写分析的准确率,将偏序Markov模型特征的主成分与校准特征融合,充分利用特征互补并降低冗余,可以在参与者中更好地识别出隐写者,从而提高识别准确率。实验结果表明,在不同隐写算法和嵌入率条件下,采用该方法比现有方法准确率平均提高约2%,最高提高约16%。     

Sharing and hiding secret images with size constraint

This paper presents a method for sharing and hiding secret images. The method is modified from the (t,n) threshold scheme. (Comput.Graph. 26(5)(2002)765) The given secret image is shared and n shadow images are thus generated. Each shadow image is hidden in an ordinary image so as not to attract an attacker's attention. Any t of the n hidden shadows can be used to recover the secret image. The size of each stego image (in which a shadow image is hidden) is about 1/t of that of the secret image, avoiding the need for much storage space and transmission time (in the sense that the total size of t stego images is about the size of the secret image). Experimental results indicate that the qualities of both the recovered secret image and the stego images that contain the hidden shadows are acceptable. The photographers who work in enemy areas can use this system to transmit photographs.     

Simple and effective approach for construction of universal blind steganalyzer

The goal of universal blind steganalysis is to detect all known (already existing) and unknown (previously unseen) steganographic algorithms without knowledge of the exact stego algorithm used by the steganographer. However, a binary blind steganalyzer trained on cover images and stego images randomly selected from “known stego images” (i.e., stego images produced by multiple “known” stego methods with a mixture of payloads), may fail catastrophically on unknown stego methods although shows superior performance on known stego methods. Additionally, unsupervised outlier detection and one-class classification approaches are less likely to fail to detect unknown stego methods but yield high false positive rates. Motivated by these observations, we explore a simple and effective approach for construction of universal blind steganalyzer to achieve overall good performance on both known and unknown stego algorithms. First, we compute Local Outlier Factor (LOF) scores of known stego sample points (feature vectors) with respect to test sample points. Then, we choose stego images with the lowest LOF scores from known stego images as training stego images. Finally, we train a binary classifier on cover images and chosen training stego images for test. Experimental results confirm that the proposed approach performs significantly better than the random sampling-based binary classification method, unsupervised outlier detection and one-class classification approaches on both known and unknown stego algorithms.     

基于多维梯度能量的空域隐写分析

隐写分析是信息安全领域一个新的研究热点,其中多数为针对特定隐藏算法的隐写分析算法,少数泛盲隐写分析算法又具有复杂度高正检率低的缺点.对图像像素值扣除受隐藏改变很小的高位后剩余的低位图像进行小波变换,恢复载体图像,利用梯度能量之差形成12维特征向量,最后通过支持向量机(SVM)进行训练分类.在3000幅训练图像库和3000幅测试图像库上(没有交集),分别对LSB(least significant bits)、自适应空域和BPCS(bit-plane complexity segmentation)等多种空域隐藏算法进行训练和测试.实验结果表明,算法有很好的检测性能,载密图像和载体图像的平均正检率分别为93.7%和96.2%.     

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。     

Sharing secrets in stego images with authentication

Recently, Lin and Tsai and Yang et al. proposed secret image sharing schemes with steganography and authentication, which divide a secret image into the shadows and embed the produced shadows in the cover images to form the stego images so as to be transmitted to authorized recipients securely. In addition, these schemes also involve their authentication mechanisms to verify the integrity of the stego images such that the secret image can be restored correctly. Unfortunately, these schemes still have two shortcomings. One is that the weak authentication cannot well protect the integrity of the stego images, so the secret image cannot be recovered completely. The other shortcoming is that the visual quality of the stego images is not good enough. To overcome such drawbacks, in this paper, we propose a novel secret image sharing scheme combining steganography and authentication based on Chinese remainder theorem (CRT). The proposed scheme not only improves the authentication ability but also enhances the visual quality of the stego images. The experimental results show that the proposed scheme is superior to the previously existing methods.     

采用SVR模型进行嵌入率估计的隐写分析方法

为解决大多数通用隐写分析算法不能检测秘密信息长度的问题,提出了一种改进的能估计秘密信息长度的通用隐写分析方法。从隐写图中提取描述DCT域系数相关性的132维特征,用支持向量回归机学习图像特征和相应嵌入改变率之间的映射关系并建立模型,根据映射模型估计测试隐写图的嵌入改变率。使用典型的嵌入算法:F5、outguess与MB进行测验,仿真结果显示提出的秘密信息长度估计算法是切实可行的。     

A two-codebook combination and three-phase block matching based image-hiding scheme with high embedding capacity

Image hiding is a technique that embeds the important images into a cover image such that the important images are imperceptible and can be securely transmitted to the receiver. In such research, the common goals are to enlarge the embedding capacity as much as possible since the visual quality of the cover image is degraded slightly and to keep high visual quality of the important images when they are extracted from the stego image. In this paper, we propose an image-hiding method based on the two-codebook combination, the three-phase block matching procedure, and the modulus substitution. The proposed method can achieve these benefits: (1) multiple, relatively large important images can be embedded into a relatively small cover image; (2) the quality of the stego image after embedding the secret data is not distorted significantly; (3) the important images have an acceptable visual quality after they are extracted. The experimental results also show that the proposed method is more flexible than previous methods.     

LSB steganographic payload location for JPEG-decompressed images

Steganalysis always concentrates on the detection of the existence of the hidden information embedded into some digital multimedia by steganography. This paper presents a novel steganographic payload location method for spatial least significant bit (LSB) steganography with the JPEG-decompressed images used as the cover images. Firstly, we discuss the possibility of recovering the original cover image and compute the probability of successfully recovering. Then, we compute the distributions of the difference values between the estimated cover pixels and stego pixels in the payload-carrying and no-payload-carrying positions. Finally, combining with the theory of hypothesis test, we present a method to locate the payload-carrying pixels of JPEG-decompressed images, which is suitable for both LSB replacement and LSB matching. The experimental results show that: 1) the number of stego images needed to accurately locate all the payload-carrying pixels in the proposed method approaches the lower bound; 2) given the number of stego images, in the proposed method, the number of the payload-carrying pixels precisely located approaches the upper bound; 3) the proposed method performs better than some current state-of-the-art steganographic payload location methods for JPEG-decompressed images.     

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...     

A steganography embedding method based on edge identification and XOR coding

In this paper, we present a novel image steganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image. Edge detection enables the identification of sharp edges in the cover image that when embedding in would cause less degradation to the image quality compared to embedding in a pre-specified set of pixels that do not differentiate between sharp and smooth areas. This is motivated by the fact that the human visual system (HVS) is less sensitive to changes in sharp contrast areas compared to uniform areas of the image. The edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stego images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stego images. In order to embed three secret message bits, the algorithm requires four bits of the cover image, but due to the coding mechanism, no more than two of the four bits will be changed when producing the stego image. The proposed method utilizes the sharpest regions of the image first and then gradually moves to the less sharp regions. Experimental results demonstrate that the proposed method has achieved better imperceptibility results than other popular steganography methods. Furthermore, when applying a textural feature steganalytic algorithm to differentiate between cover and stego images produced using various embedding rates, the proposed method maintained a good level of security compared to other steganography methods.