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．     

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.     

Audio watermarking scheme robust against desynchronization attacks based on kernel clustering

In this paper, we propose an adaptive audio watermarking scheme based on kernel fuzzy c-means (KFCM) clustering algorithm, which possesses robust ability against common signal processing and desynchronization attacks. The original audio signal is partitioned into audio frames and then each audio frame is further divided as two sub-frames. In order to resist desynchronization attacks, we embed a synchronization code into first sub-frame of each audio frame by using a mean quantization technique in temporal domain. Moreover, watermark signal is hid into DWT coefficients of second sub-frame of each audio frame by using an energy quantization technique. A local audio feature data set extracted from all audio frames is used to train a KFCM. The well-trained KFCM is used to adaptively control quantization steps in above two quantization techniques. The experimental results show the proposed scheme is robust to common signal processing (such as MP3 lossy compression, noise addition, filtering, re-sampling, re-quantizing) and desynchronization attacks (random cropping, pitch shifting, amplitude variation, time-scale modification, jittering).     

A semi-fragile watermarking scheme for authenticating audio signal based on dual-tree complex wavelet transform and discrete cosine transform

In this paper, a novel semi-fragile watermarking scheme for authenticating an audio signal based on dual-tree complex wavelet transform (DT-CWT) and discrete cosine transform (DCT) is proposed. Specifically, the watermark data are efficiently inserted into the coefficients of the low-frequency sub-band of DT-CWT taking advantages of both DCT and quantization index modulation (QIM). First, the original digital audio signal is segmented and then performed with DT-CWT. Second, based on the energy compression property, the low-frequency sub-band coefficients of the DT-CWT domain are performed with DCT, and the DC component is utilized to embed one distorted watermark bit by the QIM technique. Finally, inverse DCT and DT-CWT are orderly implemented on the watermarked coefficients of each audio segment to get a watermarked audio signal. Simulation results show that the hybrid embedding domain constructed by DT-CWT and DCT is effective, and the proposed watermarking scheme is not only inaudible, but also robust against content persistent non-malicious audio signal processing operations, such as MP3 compression, noise addition, re-sampling, re-quantization, etc. Furthermore, the proposed scheme can effectively authenticate the veracity and integrity of audio content and greatly expands the applicability of the audio watermarking scheme.     

基于量化技术的音频水印算法

针对数字音频作品版权保护的实际需要,提出一种利用二值图像作为版权标识的音频水印算法。利用伪随机序列对作为版权标识的二值图像进行置乱,同时对音频信号进行离散小波分解,然后根据置乱后的水印信息对所选取的小波系数进行量化处理,最后进行小波逆变换得到含水印信息的音频信号。实验表明,本算法对各种常见的水印攻击均具有较强的鲁棒性,可以有效解决数字音频作品版权保护等问题,具有一定的实用价值。     

基于局部极值点的音频盲水印算法

该文提出了一种新的音频水印算法。它充分利用了离散小波变换的多分辨率特性,在小波变换的细节部分搜索局部极值点,通过修改局部极值及其相邻点的幅度值,实现水印信息的嵌入。水印检测不需要参考原始音频。实验结果证明了该算法的鲁棒性和不可感知性。     

基于小波变换的数字水印隐藏与检测算法

主要研究了在数字语音信号中加入数字水印的方法,提出了一种基于小波变换的数字水印隐藏与检测算法,用这种算法隐藏水印具有很强的隐藏性,对原始语音的影响基本上察觉不出来。叠加了水印的语音在经过多种强干扰及各种信号处理的变换之后,使用本算法仍能正确检测出水印的存在,如它可以抵抗噪声干扰、去噪算法对信号进行去噪处理、语音信号的有损压缩以及信号的重新采样等。     

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.      

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 blind digital audio watermarking scheme based on EMD and UISA techniques

In this paper, a new perceptual spread spectrum audio watermarking scheme is discussed. The watermark embedding process is performed in the Empirical Mode Decomposition (EMD) domain, and the hybrid watermark extraction process is based on the combination of EMD and ISA (Independent Subspace Analysis) techniques, followed by the generic detection system, i.e. inverse perceptual filter, predictor filter and correlation based detector. Since the EMD decomposes the audio signal into several oscillating components–the intrinsic mode functions (IMF)–the watermark information can be inserted in more than one IMF, using spread spectrum modulation, allowing hence the increase of the insertion capacity. The imperceptibility of the inserted data is ensured by the use of a psychoacoustical model. The blind extraction of the watermark signal, from the received watermarked audio, consists in the separation of the watermark from the IMFs of the received audio signal. The separation is achieved by a new proposed under-determined ISA method, here referred to as UISA. The proposed hybrid watermarking system was applied to the SQAM (Sound Quality Assessment Material) audio database (Available at http://sound.media.mit.edu/mpeg4/audio/sqam/) and proved to have efficient detection performances in terms of Bit Error Rate (BER) compared to a generic perceptual spread spectrum watermarking system. The perceptual quality of the watermarked audio was objectively assessed using the PEMO-Q (Tool for objective perceptual assessment of audio quality) algorithm. Also, using our technique, we can extract the different watermarks without using any information of original signal or the inserted watermark. Experimental results exhibit that the transparency and high robustness of the watermarked audio can be achieved simultaneously with a substantial increase of the amount of information transmitted. A reliability of 1.8 10^???4 (against 1.5 10^???2 for the generic system), for a bit rate of 400 bits/s, can be achieved when the channel is not disturbed.     

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。     

一种相移键控数字图像水印算法

根据相移键控原理．提出了一种不可逆DWT域二相相位键控数字图像水印算法．水印信号为二值图像，用不同相位的正弦信号将水印调制后嵌入到数字图像DWT变换系数中；用提取的同频率正弦信号的相位进行水印提取；该方法嵌入的水印具有非常好的安全性，且水印提取不需要原始未加水印的图像．实验结果表明该方法具有很好的不可见性；对JPEG压缩、图像加噪声和图像滤波等常见的图像处理具有极强的鲁棒性．     

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。     

基于残差学习的新型不可感知水印攻击方法

传统的水印攻击方法虽然能够干扰水印信息的正确提取, 但同时会对含水印图像的视觉质量造成较大损失, 为此提出了一种基于残差学习的新型不可感知水印攻击方法. 首先, 通过构建基于卷积神经网络的水印攻击模型, 在含水印图像和无水印图像之间进行端到端非线性学习, 完成含水印图像映射到无水印图像的任务, 达到水印攻击的目的; 其次, 根据水印信息的嵌入区域选择合适数目的特征提取块以提取含水印信息的特征图. 鉴于含水印图像和无水印图像之间的差异过小, 水印攻击模型在训练过程中的可学习性受到限制, 导致模型很难收敛. 引入残差学习机制来提升水印攻击模型的收敛速度和学习能力, 通过减少残差图像(含水印图像和提取的特征图像做差)与无水印图像之间的差异来提升被攻击图像的不可感知性. 此外, 还根据DIV2K2017超分辨率数据集以及所攻击的基于四元数指数矩的鲁棒彩色图像水印算法构建了训练水印攻击模型的数据集. 实验结果表明该水印攻击模型能够在不破坏含水印图像视觉质量的前提下以高误码率实现对鲁棒水印算法的攻击.     

基于陷阱策略的音频数字水印新方法

数字水印已经成为实现多媒体版权保护的一项重要技术.提出在嵌入鲁棒性水印的同时考虑攻击的行为特征,辅以陷阱策略抵制各种攻击.水印以量化音频DCT系数的方式嵌入,在提取时针对攻击的行为特征以不同方式提取水印.算法嵌入的水印为一幅二值图像,能抵制随机裁剪、AD及DA转换、低通滤波等去同步型攻击,提取的水印可以直接分辨.与cocktail水印方法相比,所提方法的鲁棒性高,提取水印时不需要原始音频.实验结果说明我们的音频水印方法十分有效.     

A robust digital audio watermarking scheme using wavelet moment invariance

It is a challenging work to design a robust audio watermarking scheme against various attacks. Wavelet moment invariances are new features combining the moment invariant features and the wavelet features, and they have some excellent characteristics, such as the ability to capture local information, robustness against common signal processing, and the linear relationship between a signal and its wavelet moments etc. Based on wavelet moment and synchronization code, we propose a new digital audio watermarking algorithm with good auditory quality and reasonable resistance against most attacks in this paper. Firstly, the origin digital audio is segmented and then each segment is cut into two parts. Secondly, with the spatial watermarking technique, synchronization code is embedded into the statistics average value of audio samples in the first part. And then, map 1D digital audio signal in the second part into 2D form, and calculate its wavelet moments. Finally, the watermark bit is embedded into the average value of modulus of the low-order wavelet moments. Meanwhile combining the two adjacent synchronization code searching technology, the algorithm can extract the watermark without the help from the origin digital audio signal. Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signals processing such as MP3 compression, noise addition, resampling, and re-quantization etc., but also robust against the desynchronization attacks such as random cropping, amplitude variation, pitch shifting, and jittering etc.     

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.     

Ambiguity attacks on SVD audio watermarking approach using chaotic encrypted images

In this paper, we study the robustness of the proposed watermarking algorithm by Al-Nuaimy et al. (Digit Signal Process 21(6):764–779 2011) for audio signals which is based on singular value decomposition (SVD). It has been concluded that it is fundamentally flawed in its design, in that it falls to two ambiguity attacks where the extracted watermark is not the embedded one but determined by the reference watermark. In the first attack, when a watermarked audio signal is rewatermarked by an attacker’s watermark, this one can be easily extracted to claim ownership of the original audio signal. In the second attack, during the extracting process when an attacker uses the singular vectors of his watermark, he can extract the attacker’s watermark. Therefore, he can claim ownership of the watermarked audio signal. The experimental results prove that the proposed attacks create a false positive detection in watermark extraction. Therefore, Al-Nuaimy et al. algorithm cannot be used for security systems, data hiding and copyright protection.

     

Partially compressed-encrypted domain robust JPEG image watermarking

Digital media is often handled in a compressed and encrypted form in Digital Asset Management Systems. And watermarking of the compressed encrypted media items in the compressed-encrypted domain itself is required sometimes for copyright violation detection or other purposes. In this paper, we propose a robust image watermarking technique for partially compressed-encrypted JPEG images. However, arbitrary embedding of a watermark in a partially compressed encrypted image can cause drastic degradation of the quality as the underlying change may result in random decrypted values. In addition, due to the encryption the compression efficiency may become very low. Thus the challenge is to design a watermarking technique that provides good watermarked image quality and at the same time gives good compression efficiency. While the proposed technique embeds watermark in the partially compressed-encrypted domain, the extraction of watermark can be done in the encrypted or decrypted domains. The experiments show that the watermarked image quality is good and the reduction in compression efficiency is low. The proposed watermarking technique is robust to common signal processing attacks. The watermark detection performance of the proposed scheme is better than the existing encrypted domain watermarking techniques.