Improving security of quantization-index-modulation steganography in low bit-rate speech streams

In this study, we mainly concentrate on quantization-index-modulation (QIM) steganography in low bit-rate speech streams, and contribute to improve its security. Exploiting the characteristics of codebook division diversity in the complementary neighbor vertices algorithm, we first design a key-based codebook division strategy, which follows Kerckhoff’s principle and provides a better security than the previous QIM approach. Further, to resist the state-of-the-art steganalysis, following a general belief that fewer and smaller cover changes are less detectable and more secure, we present an improved QIM steganography, which introduces random position selection to adjust the embedding rate dynamically, and employs matrix encoding strategy to enhance the embedding efficiency. The proposed approach is evaluated with ITU-T G.723.1 as the codec of cover speech and compared with the previous work. The experimental results demonstrate that the proposed approach outperforms the traditional QIM approach on both steganographic transparency and steganalysis resistance. Moreover, it is worth pointing out that our approach can effectively work in conjunction with not only G.723.1 codec but also all other parametric speech coders, and be successfully applied into Voice-over-Internet-Protocol systems.     

基于贝叶斯网络的压缩语音信息隐藏检测

压缩语音量化索引调制（QIM）信息隐藏方法中,最近邻投影点QIM（NPP-QIM）方法具有较高的嵌入效率和隐蔽性。针对现有的隐写分析方法对NPP-QIM方法检测准确率不高的问题,提出了一种基于贝叶斯推理的检测方法以提高检测准确率。首先,利用矢量量化（VQ）码字（VQ₁、VQ₂、VQ₃）构建了码字时空转移网络（CSTN）;接着,以码字转移指数对CSTN进行化简得到隐写敏感码字时空转移网络（SS-CSTN）;然后,基于SS-CSTN进一步构建了码字贝叶斯网络（CBN）;最后,使用Dirichlet分布作为先验分布学习网络参数,实现对QIM信息隐藏的检测。实验结果表明,在嵌入率为100%、时长为10 s时,与索引分布特征（IDC）方法和梅尔频率倒频系数（DMFCC）方法相比,提出CBN方法的检测准确率分别提高了25个百分点和 37个百分点;在时间性能方法,检测一段10 s的语音时间约为21 ms,能够实时检测。     

低比特率语音流大容量分层隐写方法

目的 基于语音增强和丢包补偿等技术的互联网低比特率编解码器(internet low bit rate codec, iLBC)在丢包率较高的网络环境下仍具有很好的语音质量。如何在隐写容量、不可感知性和抗检测性之间达到理想均衡是iLBC音频隐写面临的难点。为此,本文提出一种基于分层的iLBC语音大容量隐写方法。方法 首先分析iLBC的编码比特流结构。然后基于主观语音质量评估指标PESQ-MOS(perceptual evaluation of speech quality-mean opinion score)和客观语音质量评估指标MCD(mel cepstral distortion)分析在线性频谱频率系数矢量量化过程、动态码本搜索过程和增益量化过程进行隐写对语音质量的影响,提出一种隐写位置分层方法,在增益量化过程和动态码本搜索过程按照嵌入容量和层次的优先级依次进行隐写,尽可能降低失真;对不能嵌满的层,提出一种基于Logistic混沌映射的嵌入位置选择方法,提升隐写的随机性和安全性。最后采用量化索引调制方法进行秘密信息嵌入,进一步提升隐写的安全性。结果 在中英文语音数据集SSD(st...     

基于浊音特性的语音压缩域隐写分析

语音压缩域中的隐写和隐写分析是一个重要的课题,目前隐写分析主要针对脉冲编码或修改线谱对的算法,对于修改基音周期的隐写则无法检测,提出了一种基于浊音特性的压缩域语音隐写分析算法.浊音具有准周期性,浊音段嵌入隐藏信息后会改变这一性质.根据这一原则,从待测的压缩语音码流中提取出延迟参数,利用层次聚类算法区分清音浊音,在此基础上针对浊音段进行游程检测,判断有无隐藏信息.实验表明,该方法对改变基音周期性的信息隐藏算法具有很好的隐写检测能力.     

Joint-channel modeling to attack QIM steganography

This paper presents a statistical steganalysis framework to attack quantization index modulation (QIM) based steganography. The quantization process is generally modeled as an additive noise channel. The proposed method exploits the fact that plain-quantization (quantization without message embedding) decreases local-randomness (or increases local-correlation) in the resulting quantized image. Moreover, QIM-stego image exhibits relatively higher level of local-randomness than the corresponding quantized-cover, though both are obtained using same set of parameters. The local-randomness (or inter-block correlation) of the test-image is used to capture traces left behind by quantization. A parametric model is developed to characterize channel-dependent local-randomness. Maximum likelihood estimation (MLE) framework is used to estimate parameters of the distribution of local-randomness mask. Distributions of parameters, estimated from the quantized-cover and the QIM-stego images, are used to characterize quantization with and without message embedding. To investigate variations in the estimated parameters as a function of frequency, inter- (variations within each channel(or subband)) and intra-channel (across all channels), joint-channel modeling and single-channel modeling, respectively, is considered. For each approach, a set of parametric detectors based on generalized likelihood ratio test (GLRT) is used to distinguish between the cover- and the stego-images. To improve detection accuracy, decisions from both detectors are fused to generate the final stego-detection decision. Effectiveness of the proposed framework is evaluated using a dataset consisting of over 35000 test-images obtained from 880 uncompressed natural images. Experimental results show that the proposed scheme can successfully detect QIM-stego images with very low false rates. In addition, performance comparison with existing state-of-the-art also shows that the proposed method performs significantly superior than the selected methods.     

一种基于线性规划特征选择和集成分类器的图像隐写分析方法

隐写分析是防范由隐写术进行信息隐藏所带来危害的有效方法。图像隐写分析方法主要用于检测图像是否被隐写术嵌入隐秘信息。通用型图像隐写分析能够针对广泛类型的隐写术进行检测,该类方法一般采用从图像提取的统计特征和分类器模型进行。当前的高性能隐写分析一般采用高维特征和集成分类器进行。高维特征能够较好地表达图像统计特性中被隐写术扰动的成分,但另一方面,高维特征具有较多的冗余和无效成分,因此进行特征选择能较好的提升效率。本文提出一种使用线性规划的特征选择模型,该模型可与集成分类器协同使用,同时考虑集成分类器中子分类器的检测精度和多个子分类器使用特征的多样性。实验证明,本文提出的方法对多个隐写术的检测性能有较好的提升。     

Perturbed quantization steganography

In this paper, we use the recently proposed wet paper codes and introduce a new approach to passive-warden steganography called perturbed quantization. In perturbed quantization, the sender hides data while processing the cover object with an information-reducing operation that involves quantization, such as lossy compression, downsampling, or A/D conversion. The unquantized values of the processed cover object are considered as side information to confine the embedding changes to those unquantized elements whose values are close to the middle of quantization intervals. This choice of the selection channel calls for wet paper codes as they enable communication with non-shared selection channel. Heuristic is presented that indicates that the proposed method provides better steganographic security than current JPEG steganographic methods. This claim is further supported by blind steganalysis of a specific case of perturbed quantization for recompressed JPEG images.     

基于深度残差网络的语音隐写分析方法

针对目前以WAV格式语音为载体的最低有效位（LSB）隐写方法的检测性能较低的问题,提出了一种基于深度残差网络的语音隐写分析方法。首先,通过多组高通滤波器组成的固定卷积层来计算输入语音信号的残差信号,并利用截断线性激活单元对得到的残差信号进行截断操作;然后,通过卷积层与设计的残差块的堆叠来构建深度网络,以提取深层次的隐写特征数据;最后,利用全连接层与Softmax层组成的分类器输出最终的分类结果。实验结果表明,在Hide4PGP和LSBmatching两种隐写方法的不同密信嵌入率下,所提出模型的检测正确率都要优于现有的基于卷积神经网络（CNN）的隐写分析方法。对于0.1 bps嵌入率的Hide4PGP隐写方法,该隐写分析模型的检测正确率比LinNet提高了近7个百分点。     

基于贝叶斯网络模型和矩阵嵌入的VoIP隐写算法

网络语音电话(voice over IP, VoIP)已成为目前人们沟通交流的普遍选择.相比文本、图像等传统隐写载体,网络语音数据流隐蔽性好,隐藏空间更大,因而得到越来越多的关注.目前的网络语音隐写研究围绕算法设计展开,已有成果的抗检测性有待进一步加强,且缺乏安全性理论指导.为此,首先分析语音帧的时序性特点,以相对熵的形式定义了基于贝叶斯网络模型的隐写安全性.通过分析语音编码过程,建立了固定码本参数的贝叶斯网络模型并将参数分为二元载体和三元载体2类.应用矩阵嵌入技术确定载体的修改位置,减少修改量;以最小化安全性测度为目标确定三元载体元素的修改方向,降低隐写对载体统计特性的影响.实验表明：在不显著增加计算复杂度的前提下,所提算法取得了比已有算法更好的感知透明性和抗检测能力.     

引入超分辨率下采样误差的图像边信息估计隐写

目的由于空域图像下采样过程中提供的量化误差边信息能够有效提升隐写安全性,为了得到下采样之前的高分辨率图像,提出一种基于超分辨率网络的空域图像边信息估计隐写方法。方法受原始下采样边信息隐写方法的启发,使用超分辨率网络生成被称为预载体的高分辨率图像。同时利用现有的空域图像对称失真算法得到每个像素点的修改失真,然后以浮点型精度对预载体下采样,得到和载体同分辨率的图像形式,利用对应像素点间的差值指导像素点的修改方向,实现基于初始失真的非对称失真调整。首先以峰值信噪比和极性估计准确率为指标对比了多种超分辨率网络以及基于传统插值方法的上采样性能,并通过调整初始失真分别进行隐写和隐写分析实验,选择使安全性提升最大的残差通道注意力机制网络及其对应调整系数作为本文的下采样边信息估计隐写方法。结果使用隐写领域中常用的3个数据库、两种传统初始失真函数以及两类隐写分析方法进行实验。在跨数据集的隐写安全性上,相比传统隐写方法,在对抗基于手工特征和基于深度学习的隐写分析时,本文方法的安全性均有显著提升,如在测试集载体图像上,嵌入率为0.5 bit/像素时,安全性分别提升6.67%和6.9%;在训练集载体图像上,本...     

Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis

This paper presents a method to detect the quantization index modulation(QIM) steganography in G.723.1 bit stream.We show that the distribution of each quantization index(codeword) in the quantization index sequence has unbalanced and correlated characteristics.We present the designs of statistical models to extract the quantitative feature vectors of these characteristics.Combining the extracted vectors with the support vector machine,we build the classifier for detecting the QIM steganography in G.723.1 bit stream.The experiment shows that the method has far better performance than the existing blind detection method which extracts the feature vector in an uncompressed domain.The recall and precision of our method are all more than 90% even for a compressed bit stream duration as low as 3.6 s.     

基于对称α稳态模型的JPEG通用隐写分析

针对JPEG隐写术检测问题,提出一种基于DCT系数分布模型的通用隐写分析方法。根据DCT系数统计分布规律,建立双参数对称α稳态模型,并将模型参数估计问题转化为离散的函数参数优化问题,利用改进的遗传算法进行求解。提取分布模型的参数作为隐写分析的特征,并运用图像校准技术进行校准以提高特征的敏感性。设计与特征相匹配的、具有线性阶计算复杂度的单类分类器进行隐写判别。实验结果表明,该方法可以有效地检测JPEG隐写术,当嵌入率为25%时,平均检测率达到76.1%,相比传统基于模型的隐写分析方法提高5.5%,具有更高的检测性能。     

基于视觉感知机理的大容量彩色图像盲隐写算法

针对现有彩色图像隐写算法大多将灰度图像的算法直接用于彩色通道,未能运用彩色视觉感知机理的问题,提出一种YUV空间上的、基于视觉感知机理的大容量彩色图像盲隐写算法。主要方法是在Y分量上通过标准差来分析载体图像的局部复杂性,利用视觉感知机理将信息分成两部分隐写于Y和V分量中,灵活性较差的U分量则作为隐写信息的通道指示器,无需大的改变。大量实验结果表明,所提算法与其他方法相比在嵌入大容量信息后依然能够保持很好的不可感知性,而且可以抵抗直方图对比和RS隐写分析方法,因而所提算法是合理、简洁、有效的。     

基于视觉模型的抗共谋指纹方案

在传统的基于QIM（Quantization Index Modulation）嵌入的数字指纹系统中,由于量化步长固定,指纹系统的抗共谋性能和图像保真度较差。针对这些问题,提出一种抗共谋指纹方案,在嵌入指纹时添加随机信号到宿主信号的DCT域,根据Watson视觉模型选择量化步长。实验结果表明,与原始的QIM指纹方案相比,采用提出方案的QIM指纹系统在抗共谋能力和视觉失真度方面都具有较好的性能。     

基于双向马尔可夫模型的JPEG图象隐写分析

提出了一种基于双向马尔可夫模型的JPEG图象的通用隐写分析方法,利用量化后分块DCT系数的中低频系数间的相关性,提取DCT块内和块间的特征,采用阈值贝叶斯分类方法进行识别,并且与SVM分类器的识别效果进行了比较。针对4种公认的JPEG嵌入方法——F5、Outguess、MB1和MB2进行隐写分析,在CorelDraw图象库上做实验,取得了很好的性能。     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

高位有效位概率算术解码的图像密写改进算法

提出一种基于MSBs(高位有效位)概率算术解码的DCT(离散余弦变换)域YASS(yet another steganographicscheme)密写改进算法。为了克服YASS算法因量化索引调制(QIM)嵌入特性造成的易攻击缺陷,在YASS消息嵌入中用原型信号MSBs概率分布约束的算术解码,使消息嵌入流的解码序列具有与原型信号一致的边缘分布,在保持YASS算法高随机性嵌入优点的同时,能使密写前后一阶分布直方图之间的差异最小化,有效保证了密写的安全性。实验结果表明,与YASS对比,该方法使系数分布直方图的平均变化量更小,其平均失真为6.3%,相比YASS的平均失真6.8%,减少了0.5%,改进算法的抗隐写分析能力比后者更强,是一种有效的密写算法。     

Steganalysis Frameworks of Embedding in Multiple Least-Significant Bits

Replacement of least-significant bit plane is one of the popular steganography techniques in digital images because of its extreme simplicity. But it is more difficult to precisely estimate the rate of secret message embedded by replacement of multiple least-significant bit (MLSB) planes of a carrier object. In order to model the MLSB embedding, a lemma is introduced to prove the transition relationships among some trace subsets. Then, based on these transition relationships, two novel steganalysis frameworks are designed to detect two kinds of distinct MLSB embedding methods. A series of experiments show that the proposed steganalysis frameworks are highly sensitive to MLSB steganography, and can estimate the rate of secret message with higher accuracy. Furthermore, these frameworks can fully meet the need to distinguish stego images under low false positive rate, especially when the embedded message is short.      

基于双层湿纸嵌入的自适应图像隐写算法

为了提高隐写系统抗统计检测的能力,对影响隐写算法统计安全性的因素进行了分析,得出减小载密图像统计失真的3条途径。以此为基础,提出一种基于双层湿纸嵌入的图像自适应隐写算法。该算法根据邻域相关性,选取局部环境噪声强的像素作为嵌入对象,利用“湿纸码(WPC)”在其最低有效位及次低有效位嵌入秘密信息。对未压缩图像库的实验表明,该方法比现有算法具有更好的抗统计检测性能。     

An improved entropy-based approach to steganalysis of compressed speech

Compared with more and more steganography techniques motivated by abundant compressed speech, steganalysis is still a challenging task. Many existing studies are based on a single dimensional feature model and it is difficult to have a wide range of applicability. In this paper, a hybrid Markov model is proposed, which is based on the correlation of fixed codebook parameters in speech codec between pulses in a given track. And then, two detecting methods based on entropy are given. One is designed as a single-pulse position based entropy detection method (SPBE). The other is pulse-position pairs based entropy detection method (PPBE). Simultaneously, to solve the problem of inaccurate calculation of the entropy rate of finite length samples, corrected conditional entropy (CCE) is used as an estimate of the Markov chain entropy rate. Experiments show that CCE and entropy are highly complementary, and both can be employed as classification features to achieve better steganalysis results. Finally, the performance of the proposed detection methods is evaluated and compared with the existing detection methods. Results prove that the two methods proposed in this paper are suitable for online and real-time steganographic detection, especially for small-size samples.