【作者】 许晓娟；

【导师】 何承源； 彭宏；

【作者基本信息】 西华大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 随着计算机网络和多媒体技术的飞速发展,多媒体数据逐渐成为人们获取信息的重要来源,并成为人们生活的重要组成部分。因而,作为数字媒体产品知识产权宣告及保护的有效工具,数字水印技术自1993年第一次提出以来已经引起了人们极大的关注,同时,如何保护多媒体信息的安全成为国际上研究的热门课题。特别是音频信号的数字水印技术已经成为近年来研究的热点之一。信息安全技术中采取的密码学技术因仅能控制信息的传播过程,而对于解码后的媒体数据却难以控制,因而无法阻止盗版者的非法拷贝和传播。作为解决上述问题的一种有效途径,数字水印技术开始引起人们的普遍关注。它通过在原始数据中嵌入秘密信息来证实数据的所有权或完整性,以此来抑制对数字作品的盗版或篡改。数字音频水印技术就是向载体数据(如音频信号)中嵌入秘密信息以达到版权宣告及保护的目的。数字水印技术的关键就是工作域和嵌入策略的选取,从某种程度上说,工作域和嵌入策略选取的好坏从根本上决定了整个数字水印系统的优劣。而近年发展起来的小波变换是一种新型的时频分析方法,具有很多良好的性质,特别适合于音频信号的处理。本文正是将小波变换应用到音频信号的数字水印系统中,提出一种小波域上基于机器学习方法的新的数字音频盲水印算法,使其能在水印的鲁棒性和不可感知性之间寻找到合理的平衡点,从而具有更良好透明性和鲁棒性,并在音频作品的版权保护中能有一定的实用性。本文的主要贡献如下:(1)提出一种基于支持向量回归机(Support Vector Regression,SVR)的鲁棒数字音频水印算法。算法的基本思想是先对整段音频信号进行分抽样处理,然后对所有的子音频分别进行小波变换,水印信号则嵌入到其中一个子音频信号的胁ū浠缓蟮牡推迪凳?水印提取时不需要原始音频信号。由于不同子音频信号之间的高度相关性,相应的DWT分解后的各小波低频系数分布也具有相似性。利用这种高度相关性,在嵌入和提取的过程应用SVR具有的非线性逼近能力,建立待嵌入的子音频信号与其他子音频信号之间的对应模板关系,再利用训练好的SVR提取水印,实现盲检测。(2)针对数字音频水印的鲁棒性和不可感知性两者之间是相互制约问题,提出一种采用遗传算法(Genetic Algorithms,GA)来解决最优嵌入能量的优化水印方案。遗传算法是通过群体进化来随机搜索目标函数的最优化问题,对于前述基于支持向量回归机的小波域数字音频盲水印算法,进一步地探讨采用遗传算在嵌入强度集合中搜索对抗攻击能力适应度较高的个体,从而得到最佳嵌入强度的一个优化方案,以实现自适应策略,也使本算法能在水印的鲁棒性和不可感知性之间寻找到合理的平衡点,从而具有更良好透明性和鲁棒性。仿真实验结果表明,两种方法都具有较强的鲁棒性和不可感知性,水印嵌入容量的自适应性,提取水印时不需要原始音频信号的参与,且对包括MP3有损压缩、低通滤波、重采样/重量化等多种攻击性试验具有较强的稳健性,是可行的数字音频水印算法。可以根据具体需求,用于数字音频作品的版权保护。更多还原

【Abstract】 The digital media has become a main way for information communication along with the rapid development of digital technology and computer networks. As a useful tool for the copyright protection and judgment, digital watermarking technique has gained more and more concerns in many ways since it appeared in 1993, meanwhile, protection of digital multimedia information has become an increasingly important issue. Especially audio digital watermarking technique has been one of the research hotspots in recent years. Traditional information security system can only safeguard information transmitting process, but which can’t control the decoded media data. So it can’t prevent the illegal copy of the pirate. As a novel way to solve these problems, digital watermarking technology begins to be popularly researched and used. By embedding some secret watermark information in the host multimedia signals, it provides solutions to copyright protection and content verification.With the digital audio watermarking technique we can embed secret information into digital audio signal, so as to arrive at the purpose of copyright protection and judgment. The chose of the work field and the embed method becomes more necessary. Wavelet transform, as a new powerful tool of time-frequency analysis, provides several good characters that make it appropriate to audio signal watermarking. So, a novel machine learning based digital blind audio watermarking scheme in the wavelet domain is proposed in this paper, which could find a reasonable balance between the robustness and inaudibility of the watermark, then have the better robustness and inaudibility. And the proposed watermarking method which doesn’t require the use of the original audio signal for watermark extraction also can provide a good copyright protection scheme.The main contribution of this paper is as follows:(1) This paper focuses mainly on a novel support vector regression (SVR) based digital audio watermarking scheme in the wavelet domain which using subsampling. The audio signal is subsampled firstly and all the sub-audios are decomposed into the wavelet domain respectively. Then the watermark information is embedded into the low-frequency region of random one sub-audio. With the high correlation among the sub-audios, accordingly, the distributing rule of different sub-audios in the wavelet domain is similar to each other, SVR can be used to learn the characteristics of them. Using the information of unmodified template positions in the low-frequency region of the wavelet domain, the SVR can be trained well. Thanks to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks, and the proposed watermarking method which doesn’t require the use of the original audio signal.(2) Aiming at the problem which is the robustness and inaudibility of the digital audio watermark is limited for each other, so an optimization watermarking method based on Genetic Algorithm (Genetic Algorithms, GA) which to compute the best energy quickly is proposed in this paper. Because the genetic algorithms could search the optimization of objective function randomly, which via the evolution of groups, our work is based on the embedding/detecting way of "DWT-Based Audio Watermarking Using Support Vector Regression and Subsampling". And this method provides a preliminary discussion on the application of GA, which searching the individual with the highest fitness of attack-resistance in the aggregate of the optimal embedding strength. As an optimization solution of obtaining the optimal embedding strength, it could achieve an adaptive policy, and find a reasonable balance between the robustness and inaudibility of the watermark, just for the better robustness and inaudibility in the algorithm.The experimental results show the two practical algorithms can preserve inaudibility and they are robust enough to against the different signal processing operations. In addition, the watermarking information can be embedded into the original audio signal adaptively, which also doesn’t require the use of the original audio signal for detection. Furthermore, it can resist the different attacks, such as lossy compression (MP3), filtering, resampling and requantizing, etc. So it can be used for copyright protection of digital audio productions.更多还原