An efficient singular value decomposition algorithm for digital audio watermarking

The singular value decomposition (SVD) mathematical technique is utilized, in this paper, for audio watermarking in time and transform domains. Firstly, the audio signal in time or an appropriate transform domain is transformed to a 2-D format. The SVD algorithm is applied on this 2-D matrix, and an image watermark is added to the matrix of singular values (SVs) with a small weight, to guarantee the possible extraction of the watermark without introducing harmful distortions to the audio signal. The transformation of the audio signal between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. A comparison study is presented in the paper between the time and transform domains as possible hosting media for watermark embedding. Experimental results are in favor of watermark embedding in the time domain if the distortion level in the audio signal is to be kept as low as possible with a high detection probability. The proposed algorithm is utilized also for embedding chaotic encrypted watermarks to increase the level of security. Experimental results show that watermarks embedded with the proposed algorithm can survive several attacks. A segment-by-segment implementation of the proposed SVD audio watermarking algorithm is also presented to enhance the detectability of the watermark in the presence of severe attacks.     

An adaptive audio watermarking based on the singular value decomposition in the wavelet domain

This paper presents a secure, robust, and blind adaptive audio watermarking algorithm based on singular value decomposition (SVD) in the discrete wavelet transform domain using synchronization code. In our algorithm, a watermark is embedded by applying a quantization-index-modulation process on the singular values in the SVD of the wavelet domain blocks. The watermarked signal is perceptually similar to the original audio signal and gives high quality output. Experimental results show that the hidden watermark data is robust to additive noise, resampling, low-pass filtering, requantization, MP3 compression, cropping, echo addition, and denoising. Performance analysis of the proposed scheme shows low error probability rates. The data embedding rate of the proposed scheme is 45.9 bps. The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes.     

A learning-based audio watermarking scheme using kernel Fisher discriminant analysis

A novel learning-based audio watermarking scheme using kernel Fisher discriminant analysis (KFDA) is proposed in this paper. Two techniques, down-sampling technique and energy relationship modulation technique, are developed in order to guarantee good fidelity of the watermarked audio signal. At the same time, local energy relationship between audio sub-frames is hid in the watermarked audio signal with watermark embedding. Moreover, a learning-based watermark detector using the KFDA is exploited and it extracts the watermark by learning the local energy relationship hid in the watermarked audio signal. Due to powerful non-linear learning ability and good generalization ability of the KFDA, the learning-based watermark detector can exhibit high robustness against common audio signal processing or attacks compared with other audio watermarking methods. In addition, it also has simple implementation and lower computation complexity.     

High payload multi-channel dual audio watermarking algorithm based on discrete wavelet transform and singular value decomposition

Digital watermarking technology is concerned with solving the problem of copyright protection, data authentication, content identification, distribution, and duplication of the digital media due to the great developments in computers and Internet technology. Recently, protection of digital audio signals has attracted the attention of researchers. This paper proposes a new audio watermarking scheme based on discrete wavelet transform (DWT), singular value decomposition (SVD), and quantization index modulation (QIM) with a synchronization code embedded with two encrypted watermark images or logos inserted into a stereo audio signal. In this algorithm, the original audio signal is split into blocks, and each block is decomposed with a two-level DWT, and then the approximate low-frequency sub-band coefficients are decomposed by SVD transform to obtain a diagonal matrix. The prepared watermarking and synchronization code bit stream is embedded into the diagonal matrix using QIM. After that, we perform inverse singular value decomposition (ISVD) and inverse discrete wavelet transform (IDWT) to obtain the watermarked audio signal. The watermark can be blindly extracted without knowledge of the original audio signal. Experimental results show that the transparency and imperceptibility of the proposed algorithm is satisfied, and that robustness is strong against popular audio signal processing attacks. High watermarking payload is achieved through the proposed scheme.     

An SVD audio watermarking approach using chaotic encrypted images

This paper presents a new approach for audio watermarking using the Singular Value Decomposition (SVD) mathematical technique. The proposed approach can be used for data hiding in the audio signals transmitted over wireless networks and for multi-level security systems as will be stated in the applications section. This approach is based on embedding a chaotic encrypted watermark in the singular values of the audio signal after transforming it into a 2-D format. The selection of the chaotic encryption algorithm for watermark encryption is attributed to its permutation nature, which resists noise, filtering, and compression attacks. After watermark embedding, the audio signal is transformed again into a 1-D format. The transformation between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. The proposed approach can be implemented on the audio signal as a whole or on a segment-by-segment basis. The segment-by-segment implementation allows embedding the same watermark several times in the audio signal, which enhances the detectability of the watermark in the presence of severe attacks. Experimental results show that the proposed audio watermarking approach maintains the high quality of the audio signal and that the watermark extraction and decryption are possible even in the presence of attacks.     

基于DCT变换及SVD处理的音频数字水印算法

提出了一种基于离散余弦变换及奇异值分解的音频水印算法。首先对二值水印图像进行奇异值分解求出奇异值,然后对所得奇异值进行基于音频信号变换域性质的调制,并对音频信号进行离散余弦变换并计算水印嵌入点,最后将经过调制的水印信号嵌入。仿真试验证明这种自适应音频数字水印算法具有稳健性和不可觉察性。     

基于奇异值分解的、抗几何失真的数字水印算法

许多现有的适用于图像的数字水印对几何失真都是很敏感的。尤其是此类失真会严重妨碍对水印的盲提取。为此，提出了一种基于奇异值分解的数字水印算法，它对常见的几何失真是稳健的。此水印是被嵌入到图像分解后的奇异值之中。根据奇异值分解的代数性质，严格证明了嵌入了水印的图像在受到转置、镜像、旋转、放大和平移等几何失真后，其奇异值是不变的。在经过了上述的几何失真、一般的信号处理操作或JPEG压缩以后，嵌入的水印能够被可靠地提取和检测。实验结果表明本算法具有很好的稳健性。     

基于人工神经网络的数字音频水印算法

提出了一种新的基于神经网络训练学习的数字音频水印算法 ,采用本算法在一段数字音频数据中隐藏了一幅不可感知的二值图像 .通过后向传播算法的神经网络训练出模板信号与嵌入了水印信号的音频之间的关系特征 ,由于神经网络具有学习和自适应的特性 ,通过训练后的神经网络几乎能够完全恢复嵌入到音频中的水印数据 .通过仿真实验结果表明该算法具有较好的鲁棒性和抵抗常用的信号处理方法的处理的能力 ,特别是在水印检测时不需要原始的音频信号 .     

基于支持向量机(SVM)的数字音频水印

提出了一种新的基于支持向量机（support vector machine,SVM）的数字音频水印算法．主要思想是在宿主音频中嵌入一段模板信息,定义模板信息与宿主音频之间的一种对应关系．将水印的检测问题转化为一个可用SVM处理的二分类问题,利用SVM对先验知识（对应关系）的学习,以达到对未知数字音频水印的正确分类检测．仿真实验结果表明,该数字音频水印具有较强的健壮性和不可感知性,在受到MP3压缩、低通滤波、重采样／量化、噪声干扰等常用信号处理方法的处理后,能正确检测出水印,同时在水印检测时不需要原始音频,实现了水印的盲检测．     

稳健局部特征非下采样小波域数字水印

目的 基于数字水印技术的音乐作品版权保护是学术界的研究热点之一,多数数字音频水印方案仅仅能够对抗简单的常规信号处理,无法有效抵抗破坏性较强的一般性去同步攻击。为此,提出了一种基于稳健局部特征的非下采样小波域数字水印算法。方法 利用非下采样小波域平滑梯度检测算子从载体音频中提取稳定的音频特征点,结合数字音频样本响应确定局部特征音频段,采用量化调制策略将数字水印信号重复嵌入局部特征音频段中。结果 选取4段典型的采样频率为44.1 kHz、量化精度为16 bit、长度为15 s的单声道数字音频信号作为原始载体进行测试,并与经典算法在不可感知性和鲁棒性两方面进行对比。结果表明,本文算法在含水印音频与原始载体音频间的信噪比平均提升了5.7 dB,同时常规攻击和去同步攻击下的平均检测率分别保持在0.925和0.913,高于大多数传统算法,表明了本文算法具有较好的不可感知性。在常规信号处理（MP3压缩、重新量化、重新采样等）和去同步攻击（幅度缩放、随机剪切、音调伸缩、DA/AD转换、抖动等）方面均具有较好的鲁棒性。结论 本文利用描述能力强且性能稳定的平滑梯度刻画局部数字音频性质,提出一种基于平滑梯度的非下采样小波域音频特征点提取方法,有效解决了音频特征点稳定性差且分布极不均匀的缺点,提高了数字音频水印对音调伸缩、随机剪切、抖动等攻击的抵抗能力。     

A new robust and secure digital image watermarking scheme based on the integer wavelet transform and singular value decomposition

In this paper, a new robust and secure digital image watermarking scheme that can be used for copyright protection is proposed. The scheme uses the integer wavelet transform (IWT) and singular value decomposition (SVD). The grey image watermark pixels values are embedded directly into the singular values of the 1-level IWT decomposed sub-bands. Experimental results demonstrate the effectiveness of the proposed scheme in terms of robustness, imperceptibility and capacity due to the IWT and SVD properties. A challenge due to the false positive problem which may be faced by most of SVD-based watermarking schemes has been solved in this work by adopting a digital signature into the watermarked image. The proposed digital signature mechanism is applied to generate and embed a digital signature after embedding the watermarks; the ownership is then authenticated before extracting watermarks. Thus, the proposed scheme achieved the security issue where the false positive problem is solved, in addition to that, the scheme is considered as a blind scheme. A computer simulation is used to verify the feasibility of the proposed scheme and its robustness against various types of attacks and to compare it with some previous schemes. Furthermore, the statistical Wilcoxon signed rank test is employed to certify the effectiveness of the proposed scheme.     

A New Adaptive Digital Audio Watermarking Based on Support Vector Regression

On the basis of support vector regression (SVR), a new adaptive blind digital audio watermarking algorithm is proposed. This algorithm embeds the template information and watermark signal into the original audio by adaptive quantization according to the local audio correlation and human auditory masking. The procedure of watermark extraction is as follows. First, the corresponding features of template and watermark are extracted from the watermarked audio. Then, the corresponding feature of template is selected as training sample to train SVR and an SVR model is returned. Finally, the actual outputs are predicted according to the corresponding feature of watermark, and the digital watermark is recovered from the watermarked audio by using the well-trained SVR. Experimental results show that our audio watermarking scheme is not only inaudible, but also robust against various common signal processing (such as noise adding, resampling, requantization, and MP3 compression), and also has high practicability. In addition, the algorithm can extract the watermark without the help of the original digital audio signal, and the performance of it is better than other SVM audio watermarking schemes.     

A low-cost method for reliable ownership identification of medical images using SVM and Lagrange duality

Prevention of false positive and false negative errors is a major challenge for ownership identification and proof of ownership applications using digital image watermarking. Such errors are more critical with sensitive data, such as electronic patient records (EPRs) in medical image watermarking. A false positive error is a watermark detection error, which means that a watermark is detected in a media where there is no watermark. In contrast, a false negative error is an inability of the watermark detector to detect an embedded watermark in a watermarked image. These errors make ownership assessments unreliable, and the incorrect ownership identification of a patient’s record could result in failure of the correct diagnostics and treatments. To address this type of problem, a low-cost technique based on a support vector machine (SVM) and Lagrange duality was proposed to achieve reliable approximations for ownership identification in medical image watermarking without requiring the correction of attacked watermarked images. In this technique, the results of the ownership evaluation are categorized into two independent classes, namely watermark-detected and watermark-not-detected, and higher geometric margins between these classes are associated with higher reliability. To address additional situations with false positive and false negative errors, four different situations, including watermarked, unwatermarked, attacked watermarked and attacked unwatermarked images, were investigated. Experiments were conducted on duo-ISB-bit-plane (BiISB) watermarking using the histogram intersection (HI) technique as a testing platform under JPEG2000 and JPEG image compression attacks and using two groups of images: standard image processing images and X-ray medical images. The experimental investigations revealed that the HI technique guarantees that the rightful owner can be reliably identified even after severe attacks and in the face of context similarities between the watermark and the embedding pixels of the host image.     

An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization

Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes.     

Robust logo watermarking using biometrics inspired key generation

In this paper, a novel logo watermarking technique with key concept is proposed using fractional wavelet packet transform (FrWPT), non-linear chaotic map and singular value decomposition (SVD). The core idea is to use biometrically generated keys in the embedding process of gray-scale watermark. Therefore, this paper first proposes a method for generating keys from biometrics efficiently. Then the host image is first randomized with the help of non-linear chaotic map followed by the embedding in the FrWPT domain by modifying the singular values of the randomized image. Further, in order to enhance the security, an authentication key is formed to authenticate the watermarked image. Finally, a reliable extraction process is proposed to extracted watermark from the possibly attacked authenticate watermarked image. The security, attack and comparative analysis confirm high security, efficiency and robustness of the proposed watermarking technique. Further, an efficient solution is also proposed to deal with the ambiguous situations created by SVD in watermarking.     

An audio watermarking scheme using singular value decomposition and dither-modulation quantization

Quantization index modulation is one of the best methods for performing blind watermarking, due to its simplicity and good rate-distortion-robustness trade-offs. In this paper, a new audio watermarking algorithm based on singular value decomposition and dither-modulation quantization is presented. The watermark is embedded using dither-modulation quantization of the singular values of the blocks of the host audio signal. The watermark can be blindly extracted without the knowledge of the original audio signal. Subjective and objective tests confirm high imperceptibility achieved by the proposed scheme. Moreover, the scheme is quite robust against attacks including additive white Gaussian noise, MP3 compression, resampling, low-pass filtering, requantization, cropping, echo addition and denoising. The watermark data payload of the algorithm is 196 bps. Performance analysis of the proposed scheme shows low error probability rates.     

实离散分数Krawtchouk变换及其在数字图像水印中的应用

目的传统Krawtchouk变换处理图像时存在变换域信息表示单一、不可调节等问题,传统分数阶Krawtchouk变换处理实信号表示有冗余等问题。为了构造更加灵活的图像变换域表示,提出了实离散分数阶Krawtchouk变换,并应用于数字图像水印。方法利用对传统Krawtchouk变换矩阵特征值分解的方式,通过对分解后的特征值矩阵的实矩阵分数化构造得到了实离散分数阶Krawtchouk变换的变换矩阵,从而构造出实离散分数阶Krawtchouk变换。然后,利用所提变换从实数域变换到实数域这一特性,提出了在实离散分数阶Krawtchouk变换嵌入水印信息的图像水印算法。算法采用了图像分块处理的方式,对每个图像块的实离散分数Krawtchouk变换系数进行奇异值分解,将水印信息嵌入奇异值分解矩阵中,然后进行逆向实离散分数阶Krawtchouk变换得到嵌入水印后的图像。结果通过比较所提实离散分数阶Krawtchouk变换域水印算法和传统Krawtchouk变换域水印算法,提取水印的平均错误率在中值滤波、均值滤波、高斯滤波、JPEG压缩和缩放攻击下,分别降低了12.39%、10.04%、18.50%...     

基于DSP的嵌入式数字水印系统研究与设计

针对彩色图像进行抗几何攻击水印算法研究,利用奇异值的稳定性,结合小波变换理论,给出了一种基于DWT-SVD的抗几何攻击的彩色图像水印算法。该算法将水印信息嵌入在载体图像CIEL＊a＊b＊颜色模型的亮度空间中,并对载体图像进行打印机输出和各种攻击处理;然后,利用TMS320DM642硬件平台实时采集含水印图像,进行水印的检测与提取;最后利用Matlab对采集的载体图像和提取的水印图像进行性能评估。各种实验结果表明,基于DSP实现的本水印算法对抵抗打印扫描过程中的几何攻击具有很好的鲁棒性。     

Histogram-Based Audio Watermarking Against Time-Scale Modification and Cropping Attacks

In audio watermarking area, the robustness against desynchronization attacks, such as TSM (Time-Scale Modification) and random cropping operations, is still one of the most challenging issues. In this paper, we present a multibit robust audio watermarking solution for such a problem by using the insensitivity of the audio histogram shape and the modified mean to TSM and cropping operations. We address the insensitivity property in both mathematical analysis and experimental testing by representing the histogram shape as the relative relations in the number of samples among groups of three neighboring bins. By reassigning the number of samples in groups of three neighboring bins, the watermark sequence is successfully embedded. In the embedding process, the histogram is extracted from a selected amplitude range by referring to the mean in such a way that the watermark will be able to be resistant to amplitude scaling and avoid exhaustive search in the extraction process. The watermarked audio signal is perceptibly similar to the original one. Experimental results demonstrate that the hidden message is very robust to TSM and random cropping attacks, and also has a satisfactory robustness for those common audio signal processing operations.