基于人工神经网络的“零数字”音频水印算法

提出了一种基于人工神经网络的数字音频水印算法.利用人工神经网络学习和自适应的特征,将音频信号的重要特征作为人工神经网络的输入,通过人工神经网络的学习,建立音频信号与水印信息的对应关系,达到在不修改音频信号情况下把水印"嵌入"到原始音频信号中.实验结果表明该算法具有很强的鲁棒性和抵抗常用信号处理方法处理的能力,并且在提取水印时不需要原始音频信号.     

增强鲁棒性的音频水印算法

提出了一种基于离散小波变换的数字音频水印盲检算法.算法选择在小波系数中嵌入水印,将同步化位元码引入到音频水印的研究中,针对切割或插入额外音频段的攻击有较好的抵抗能力,并且通过重复地将水印嵌入到音频信号中,增强了鲁棒性,提取水印时不需要原始音频信号.实验结果表明,该方法具有良好的鲁棒性和不可听见性.     

数字音频信号的水印嵌入算法

文中主要研究了数字音频信号嵌入数字水印的基本原理。在此基础上应用小波变换实现数字音频信号完全可恢复数字水印算法。这种算法所隐藏的水印具有隐蔽性，基本上觉察不出对原始音频信号的影响。嵌入水印的数字音频信号经过信号处理后，仍能正确地提取数字水印。     

基于对象传播神经网络的抗TSM攻击音频水印算法*

提出了一种基于对象传播神经网络的抗TSM攻击音频水印算法。利用CPN自学习和自适应的特征,通过自适应改变段长的分段算法,选用具有较强稳定性的小波低频系数方差作为输入向量训练CPN,建立音频特征与水印信号的对应关系,以达到嵌入水印的目的。实验结果表明,该算法对常规音频信号处理和TSM等同步攻击具有很强的鲁棒性。     

数字音频信号的脆弱水印嵌入算法

该文给出了量化系数嵌入水印比特信息的基本原理，在此基础上提出了数字音频信号的脆弱水印嵌入算法。该算法充分利用离散子波变换的多分辨率特性，通过等概率随机量化音频信号不同子带的子波系数嵌入视觉可辨别的水印－二值图像。仿真实验结果表明，该文提出的数字音频信号脆弱水印嵌入算法对有损压缩、滤波和重新采样等操作具有很强的敏感性。     

一种基于二维码的鲁棒性音频水印

基于普通图像的数字音频水印算法,鲁棒性弱以及自动化检测复杂.针对这种情况,本文在此基础上提出了一种新的盲水印嵌入算法.利用二维码(QR码)自身的纠错能力,将QR码作为待嵌入的水印图像.首先将水印图像进行分块处理,以Arnold变换为基础,通过添加密匙来提升数字音频水印的安全性.其次对原始音频信号完成分帧预处理后,首先对每帧信号应用3级离散小波变换(DWT),选取低频分量进行离散余弦变换(DCT),然后把得到的一维信号进行奇异值分解.最后通过对奇异值的量化,并利用重复码的特点,将水印信息进行嵌入.实验结果表明,本文算法对噪声、低通滤波、压缩等常见的攻击方法具有良好的鲁棒性.     

一种基于倒谱变换的自同步音频水印算法

本文提出了一种基于倒谱变换的自同步数字音频水印算法。算法对音频载体分段后,将同步码嵌入到时域上,将水印信息嵌入到变换域上。水印的提取不需要原始音频信号,是一种盲水印算法。实验结果表明,嵌入后的水印不仅具有很好的不可感知性,而且对添加噪声、重新采样、低通滤波、重新量化和剪切攻击也具有很好的稳健性。     

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．     

基于听觉效果的数字音频水印算法的设计

为了提高音频水印嵌入的数据量和嵌入的效果,提出了一种基于人耳听觉效果的数字音频水印嵌入算法;该算法利用人耳对声音的滞后掩蔽效应,在声音中的一些被屏蔽掉的区域嵌入水印信息,达到扩大水印信息量和减少对音频信息的影响;设计的算法采用过零率和音频信号能量实现水印的嵌入,使用同步信号实现水印信息的定位,利用水印信号和帧段数量的相互关系来实现水印的提取;测试结果表明,该算法具有较强的鲁棒性,能够抵御典型的水印攻击技术。     

一种用于内容完整性保护的脆弱数字音频水印算法

提出了一种用于内容完整性保护的脆弱数字音频水印算法 ,该算法首先将视觉可辨的二值水印图像降维成一维水印序列并对其进行随机置乱 ,再从原始数字音频信号中随机选取采样数据并进行快速傅立叶变换 (FFT) ,最后结合人类听觉系统 (H AS)计算可感知噪声阈值 JN D(Just Noticeable Distortion,或称为临界可见失真 ) ,并依此选取FFT系数通过量化处理嵌入水印信息 .该算法提取水印信息时不需要原始数字音频信号 .仿真结果表明 :该脆弱音频水印算法不仅具有较好的透明性 ,而且对诸如叠加噪声、有损压缩、低通滤波、重新采样、重新量化等攻击均具有较好的鲁棒性     

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.     

一种基于人工神经网络的音频水印算法

本文提出了一种新的基于人工神经网络的音频水印算法。该算法利用人工神经网络来智能地估计原宿主信号的水印强度因子,并以这些强度因子的值作为确定水印嵌入强度的依据,以此种方式嵌入水印后的信号其能量频谱能够总是保持在宿主信号的最小听觉掩蔽门限之下。实验结果表明,在音频作品里面以这样的方式加入水印,不但能够保持相当好的透明性,而且对诸如加噪、重采样、重新量化等攻击表现出较好的稳健性。     

基于模数运算的DWT域数字音频水印

提出了一种基于DWT变换域的数字音频水印算法，对小波变换系数采取模2取余方法作为数字水印嵌入策略。通过仿真实验结果表明主要具有以下几个方面的特点：(1)水印隐蔽效果好，凭借人的感觉系统无法分辨出与原始音频的差别；(2)可以有效地抵抗噪声、低通滤波、有损压缩的处理；(3)在检测水印要受到密钥的限制，不知道口令的人是无法正确恢复出水印图像的。另外本算法在检测水印时不需要原始的音频信号。     

A new DCT audio watermarking scheme based on preliminary MP3 study

In this paper, a new audio watermarking scheme operating in the frequency domain and based on neural network architecture is described. The watermark is hidden into the middle frequency band after performing a Discrete Cosine transform (DCT). Embedding and extraction of the watermark are based on the use of a back-propagation neural network (BPNN) architecture. In addition, the selection of frequencies and the block hiding the watermark are based on a preliminary study of the effect of MP3 compression at several rates on the signal. Experimental results show that the proposed technique presents good robustness and perceptual quality results. We also investigate the application of the proposed technique in video watermarking. Traditional techniques have used audio channel as supplementary embedding space and adopt state-of-the art techniques that resist to MP3 compression attack. In these techniques, the MPEG compression attack is only evaluated on the video part and the audio part is kept unaffected. In this paper, we adapt the preliminary MP3 study to video watermarking technique but with a preliminary study of the MPEG compression applied to the audio channel. Here again, we notice that the application of the preliminary MPEG study to the audio channel improves the robustness of the video watermarking scheme though keeping high-quality watermarked video sequences.     

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.     

Robust Multiplicative Patchwork Method for Audio Watermarking

This paper presents a Multiplicative Patchwork Method (MPM) for audio watermarking. The watermark signal is embedded by selecting two subsets of the host signal features and modifying one subset multiplicatively regarding the watermark data, whereas another subset is left unchanged. The method is implemented in wavelet domain and approximation coefficients are used to embed data. In order to have an error-free detection, the watermark data is inserted only in the frames where the ratio of the energy of subsets is between two predefined values. Also in order to control the inaudibility of watermark insertion, we use an iterative algorithm to reach a desired quality for the watermarked audio signal. The quality of watermarked signal is evaluated in each iteration using Perceptual Evaluation of Audio Quality (PEAQ) method. The probability of error is also derived for the watermarking scheme and simulation results prove the validity of the analytical derivations. Simulation results show that MPM is robust against various common attacks such as noise addition, filtering, echo, MP3 compression, etc. In comparison to the original patchwork method and its modified versions, and some recent methods, MPM provides more robustness and inaudibility of the watermark insertion.      

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.