Noniterative Algorithms for Sensitivity Analysis Attacks

Sensitivity analysis attacks constitute a powerful family of watermark "removal" attacks. They exploit vulnerability in some watermarking protocols: the attacker's unlimited access to the watermark detector. This paper proposes a mathematical framework for designing sensitivity analysis attacks and focuses on additive spread-spectrum embedding schemes. The detectors under attack range in complexity from basic correlation detectors to normalized correlation detectors and maximum-likelihood (ML) detectors. The new algorithms precisely estimate and then eliminate the watermark from the watermarked signal. This is accomplished by exploiting geometric properties of the detection boundary and the information leaked by the detector. Several important extensions are presented, including the case of a partially unknown detection function, and the case of constrained detector inputs. In contrast with previous art, our algorithms are noniterative and require, at most, O(n) detection operations in order to precisely estimate the watermark, where n is the dimension of the signal. The cost of each detection operation is O(n); hence, the algorithms can be executed in quadratic time. The method is illustrated with an application to image watermarking using an ML detector based on a generalized Gaussian model for images     

基于导入函数引用次数的扩频软件水印方案

在研究扩频软件水印基本原理的基础上,提出一种新的扩频软件水印方案.该方案将软件导入函数引用次数作为软件的特征矢量,通过修改矢量各分量值将水印信号分散隐藏在软件整个代码内,并用相关性检测模型检测水印.给出水印信号的嵌入和检测模型,并对方案的抗攻击性和鲁棒性进行讨论.结果表明,与现有软件水印相比,该方案隐蔽性较高,实现应用比较简单,且能够有效抵抗多种常见类型的攻击.     

A novel sequential watermark detection model for efficient traceback of secret network attack flows

Network watermarking schemes have been proposed to trace secret network attack flows transferred through stepping stones as well as anonymous channels. However, most existing network flow watermark detection techniques focus on a fixed sample size of network data to achieve the required accuracy. Irrespective of the uncertainty or information content of successive observations, such detection techniques will result in low efficiency of watermark detection. We herein propose a novel sequential watermark detection model (SWDM) supporting three sequential detectors for efficient traceback of network attack flows. By exploiting the sequential probability ratio test approach, we first propose the intuitive paired-intervals-based optimum watermark detector (POWD) and the single-interval-based optimum watermark detector (SOWD) under the assumption of known parameters of the observed attack flow. We then propose the sequential sign watermark detector (SSWD) that operates on two-level quantized observations for nonparametric watermark detection. Based on our SWDM model, a statistical analysis of sequential detectors, with no assumptions or limitations concerning the distribution of the timing of packets, proves their effectiveness despite traffic timing perturbations. The experiments using a large number of synthetically-generated SSH traffic flows demonstrate that there is a significant advantage in using our sequential watermark detectors based on the proposed SWDM model over the existing fixed sample size watermark detector (FSWD). Compared to the FSWD detector, the POWD detector achieves almost 28% savings in the average number of packets. Especially, given the required probability of detection errors, the SOWD detector and the SSWD detector can achieve almost 47% and 29% savings, respectively, in the average number of required packets, thus resulting in not only guaranteed rates of detection errors but also high efficiency of flow traceback.     

基于正交水印的盲水印检测技术

提出一种稳健的盲水印检测技术.利用水印信号与主数据之间的正交性,水印检测时 不需要使用原始数据并且可以彻底消除主数据噪声的干扰,从而大大提高了水印检测器的稳健 性.无论是根据Neyman-Pearson准则还是最小错误概率准则,理论分析表明本文检测器在性能 上可以取得很大改善.利用对水印加权的分组技术,盲水印检测器在性能上逼近非盲水印检测 器.各种失真下的实验结果表明这种盲水印检测技术是有效的.     

Statistically Robust Detection of Multiplicative Spread-Spectrum Watermarks

The uncertainties in host signal modeling due to inherent model errors and various attack distortions have prompted the introduction of robust statistics theory in the context of watermark detection. Specifically, the epsi-contamination model was applied to describe the host signals, and statistically robust (SR) watermark detectors assuming known embedding strengths were derived. In this work, we investigate the robust detection structure for multiplicative watermarking. A detection-simulation (DS)-based approach to determine the contamination factor is also presented. Moreover, considering that the strengths of the watermark signals may be adapted to host signals and will very likely change after being distorted by attacks, we go further to propose the asymptotically robust detector for multiplicative watermarks, which can be viewed as the SR counterpart of the locally most powerful watermark detector in the same sense that the SR detector with full knowledge of the watermark strengths is the corresponding parallel for the optimum detector. Experiments on real images demonstrate the superiority of the new schemes over the conventional ones     

Optimum Detection for Spread-Spectrum Watermarking That Employs Self-Masking

Digital watermarking is an efficient and promising approach to protect intellectual property rights of digital media. Spread spectrum (SS) is one of the most widely used image watermarking schemes because of its robustness against attacks and its support for the exploitation of the properties of the human visual system (HVS). To maximize the watermark strength without introducing visual artifacts, in SS watermarking, the watermark signal is usually modulated by the just-noticeable difference (JND) of the host image. In advanced perceptual models, the JND is characterized as a nonlinear function of local image features. The optimum detection scheme for such nonlinearly embedded watermarks, however, has rarely been studied. In this paper, we address this problem and propose a novel approach that transforms the test signal to a perceptually uniform domain and then performs Bayesian hypothesis testing in that domain. Locally optimum detectors for arbitrary host signal distributions and arbitrary JND models that exploit the self-masking property of the HVS are derived in closed forms, in which the test signal is first nonlinearly preprocessed before a linear correlator is applied. The optimality of the proposed detector is justified mathematically according to the Neyman-Pearson criterion. Simulation results demonstrate the superior performances of the proposed detector over the conventional linear correlation detector.     

基于扩频技术联合小波算法的安全通信方案设计

在信息化技术高度发达的今天,为了适应变化莫测的战场环境对于安全信息通信的高要求,文章在研究探讨水印嵌入检测技术的基础上,利用秘钥产生的扩频码调制水印信息,紧接着通过小波变换对调制后的水印进行信息嵌入操作,而在接收端采用相反的操作对加密信息进行计算机检测后解调恢复,实现信息的隐秘传输;通过实验仿真证明了所提方案可以有效实现对信息的隐蔽传输,其具有很好的安全性,同时对于剪裁攻击具有一定的鲁棒性。     

基于CDMA扩频技术的图象水印算法

为了增强水印的稳健性和提高运算速度,提出了一种应用直接序列扩频的码分多址通信技术(DS-CDMA)的图象离散小波变换域水印算法.该算法先采用m序列优选对来生成正交的Gold序列集,然后将改进后的Gold序列对水印信息进行扩频,并形成互相叠加的.CDMA编码,最后将其嵌入到原始图象的DWT变换域.在水印提取时,则利用正交码的自相关函数特性来检测出扩频到Gold码的水印信息,从而实现了隐藏信息的盲检测.与已报道的同类算法的性能比较表明,应用该算法隐藏的水印信息量较大,且水印稳健性好.     

Multiplicative watermarking of audio in DFT magnitude

In this paper, watermark is multiplicatively embedded in discrete fourier transform magnitude of audio signal using spread spectrum based technique. A new perceptual model for magnitude of discrete fourier transform coefficients is developed which finds the regions of highest watermark embedding capacity with least perceptual distortion. Theoretical evaluation of detector performance using correlation detector and likelihood ratio detector is undertaken under the assumption that host feature follows Weibull distribution. Also, experimental results are presented in order to show the performance of the proposed scheme under various attacks such as presence of multiple watermarks, additive white gaussian noise and audio compression.     

复倒谱域自适应音频盲水印技术

提出了一种复倒谱域的自适应音频盲数字水印技术,水印嵌入过程在音频的复倒谱域中进行,并采用循环嵌入,嵌入的强度由实际应用中检测过程的具体要求控制。该文采用原始音频的一个或两个特征参数估计引起失步的参数,在水印检测前利用估计的参数对其进行同步恢复,重新获得检测和嵌入的同步。水印采用的是具有实际意义的音频。检测过程中采用独立分量分析技术,在不需要任何原始音频、水印、嵌入的信息以及攻击等信息的情况下,可以检测到水印并可精确提取水印,实现了真正意义上的盲检测。实验数据说明这种水印算法具有很好的鲁棒性。     

Invariant image watermarking using multi-scale Harris detector and wavelet moments

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. It is a challenging work to design a robust image watermarking scheme against desynchronization attacks. Based on multi-scale Harris detector and wavelet moment theory, we propose a new content based image watermarking algorithm with low computational complexity, good visual quality and reasonable resistance toward desynchronization attacks in this paper. Firstly, the steady image feature points are extracted from the origin host by using multi-scale Harris detector, and the local feature regions (LFRs) are constructed adaptively according to the feature scale theory. Then, the LFRs are image normalized, and significant regions are obtained from the normalized LFRs by utilizing the invariant centroid theory. Finally, the digital watermark is embedded into the LFRs by modifying wavelet moment invariants of the significant regions. 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 as sharpening, noise adding, and JPEG compression etc, but also robust against the desynchronization attacks such as rotation, translation, scaling, row or column removal, cropping, and local random bend etc.     

基于弱信号检测理论的离散小波变换域数字水印盲检测算法

水印检测是数字水印技术的一个关键步骤，但是目前所提出的绝大多数水印检测算法既不具备赖以支撑的理论基础，其检测性能也不是优化的．利用数字水印的不可感知特性，应用弱信号检测理论进行水印检测的研究．首先从图像小波变换系数的统计特性出发，利用广义高斯分布（GGD）来建立小波细节子带系数的统计分布模型；然后针对水印嵌入强度未知的情况，将水印检测问题转化为非高斯噪声中具有未知参数的确定性信号的检测，在弱信号条件下提出一种离散小波变换（DWT）域图像水印的优化盲检测算法．检测性能分析表明，该检测算法能够实现恒虚警率水印检测．实验结果验证了检测性能理论分析的有效性和实际性能的优越性．     

Watermarking via optimization algorithms for quantizing randomized semi-global image statistics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global statistics of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is effectively carried out by embedding to the host a computed sequence, which is obtained by solving an optimization problem whose parameters are known to the information hider but unknown to the attacker. An essential emphasis of the proposed method is randomization, which is crucial for security and robustness against arbitrary quality-preserving attacks. We formally show that malicious optimal estimation attacks that are specifically derived for our algorithm are ineffective in practice. Furthermore, we experimentally demonstrate that our watermarking method survives many generic benchmark attacks for a large number of images.     

基于马尔可夫链蒙特卡罗采样的双密钥盲水印算法

为了更好地提高水印算法的安全性,提出了一种基于两种形式密钥的强鲁棒盲水印算法。首先对水印加密,然后将每块载体的第一个奇异值组成矩阵Q再分块离散小波变换(DWT)获得四个子带,通过对四个子带进行马尔可夫链蒙特卡罗(MCMC)采样决定第k个水印位量化嵌入到矩阵Q的第k块低频、水平、垂直和高频子带中的一个并记录当前嵌入子带的密钥位,这样做不仅使水印位随机分配,而且提高了水印算法的安全性。实验结果表明,所提算法在满足不可见性的条件下,不仅对常规的图像攻击具备较强的鲁棒性,而且在水印嵌入过程中通过MCMC采样实现了用不同的密钥嵌入,提高了水印算法的安全性。     

基于小波域HMM模型的自适应图像水印算法

An adaptive image watermarking algorithm based on HMM in wavelet domain is proposed. The algorithm is abstracted as follows: 1) the vector HMM model is employed to describe the statistical characteristic of image wavelet coefficients and the resulting HMM based detector achieves significant improvements in performance compared to the conventional correlation detector; 2) adaptive watermark embedding based on HVS analysis; 3) a novel embedding strategy which is optimized for the HMM tree structure is adopted; 4) the strategy of dynamical threshold is applied in watermark detection. High robust results are achieved against Stirmark attacks, such as JPEG compression, adding noise, median cut and filter.     

An Adaptive Image Watermarking Algorithm Based on HMM in Wavelet Domain

An adaptive image watermarking algorithm based on HMM in wavelet domain is pro- posed.The algorithm is abstracted as follows:1)the vector HMM model is employed to describe the statistical characteristic of image wavelet coefficients and the resulting HMM based detector achieves significant improvements in performance compared to the conventional correlation detector; 2)adaptive watermark embedding based on HVS analysis;3)a novel embedding strategy which is optimized for the HMM tree structure is adopted;4)the strategy of dynamical threshold is applied in watermark detection.High robust results are achieved against Stirmark attacks,such as JPEG compression,adding noise,median cut and filter.     

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.     

抗几何失真的局部数字水印算法

抗几何攻击一直是数字水印中的难点问题,为了更好地解决这个问题,基于图像的几何不变域,提出了一种抗几何失真的局部数字水印算法.算法利用显著性检测器检测到的具有平移、缩放和旋转不变性特性的圆形显著区域来嵌入水印.如何利用显著区域构造不变区域以获得平移、缩放及旋转不变性,以及如何从不变区域中筛选出适合嵌水印的区域,为要解决的重点问题,首先将水印进行几何变换来匹配各个水印嵌入区域的形状,再不可感知地嵌入到各个水印嵌入区域中.还着重讨论了如何利用显著区域获得平移、缩放及旋转不变性.实验表明该算法能很好地抵抗一般的信号处理攻击及旋转、缩放、裁剪和线性变换等几何攻击.     

.基于时间间隔的扩频流水印技术

将基于时间间隔的数据流调制方法和扩频通信原理相结合,提出基于时间间隔的扩频流水印技术,对于不同的待嵌水印位使用不同的正交伪噪声码进行扩展,通过调制包到达时间的统计特征将扩展水印信息嵌入到随机选择的时间间隔中。理论分析和实验结果表明,该技术可有效进行多流追踪,并能抵御多流和均方自相关攻击。     

基于序贯假设校验的优化水印检测方案

数字水印在许多实际应用中,会有不同的侧重,如用之于数码相机、DVD、大型图像及多媒体数据库等情况下,自然以嵌入少和检测快为重点,这样可减少空间占用和显著提高水印检测效率,同时还可降低对宿主对象的品质影响,为此,本文提出的基于序贯假设检验的算法,以空域水印为对象,在保证水印提取检测时有足够小的虚警概率和漏警概率的前提下确定水印的最少嵌入量,并通过同条件的对比实验说明,本方法有明显的优化效果。