数字指纹中的共谋攻击优化算法研究
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摘要
随着互联网和多媒体技术的快速发展,特别是大量社交网络的出现,使得互联网上多媒体内容的数量急剧增长。人们可以随时随地从互联网上分享和获得多媒体信息,这给人们带来便利的同时,也引起一系列社会问题：大量图像和视频被非法使用、盗版与传播,导致知识产权纠纷及用户隐私信息泄露等诸多问题。如何保护网络多媒体的数字版权,防止版权作品的非法散布与传播,是当前亟待解决的问题。
数字指纹技术是解决这类问题的一种有效和极具潜力的技术。它通过向待分发的多媒体作品中嵌入唯一性标识(即指纹),实现从非法的可疑拷贝中追踪到原始购买者的目的。然而,共谋攻击是数字指纹系统面临的主要威胁。在共谋攻击中,多个共谋者通过合并各自的指纹样本以便削弱或移除其中的数字指纹,从而达到逃避追踪的目的。因此,研究新的共谋攻击算法,对理解当前的数字指纹技术所存在的缺陷以便找到正确的应对方案具有重要意义。
首先,以经典的数字指纹算法及常见的共谋攻击算法为研究对象,在对数字指纹系统安全性分析的基础上,建立了一个通用的数字指纹安全分析与评测框架。在此框架之下,可以对各种数字指纹算法在抵抗常见的线性和非线性共谋攻击时的性能进行全面的分析与评测。
然后,以共谋攻击作为研究重点,从三个不同的角度考虑,提出了三种新的共谋攻击优化算法。首先结合Voronoi图的几何表示,提出了一种迭代优化共谋攻击算法。该算法利用迭代优化的方法,使得原本属于Voronoi图中某个检测区域的指纹信号,经过多次迭代之后,最终落在无指纹存在的检测区域。其次,以四种经典的数字指纹算法作为攻击目标对提出的迭代优化共谋攻击算法的攻击性能和攻击之后作品的视觉质量进行了实验比较与分析。
尽管迭代优化共谋攻击可以有效的移除作品中的指纹,但是也给攻击者之后的作品带来了较大的视觉失真。为了提高攻击之后作品的视觉质量,基于添加噪声可以干扰指纹检测的事实,提出了一种自适应噪声优化共谋攻击。该算法从理论上分析了添加到指纹样本中的不同能量的高斯噪声对指纹检测器的性能以及样本视觉质量的影响。然后,以闭环反馈控制原理为指导,通过自适应的动态优化方法不断的向平均之后指纹作品中添加噪声,直到获得没有指纹的作品。实验结果表明,该算法仅需要少个三个嵌有不同指纹的作品便可以完全的移除所有共谋者的指纹。
从指纹嵌入的角度来看,嵌入在作品中的数字指纹实际上相当于叠加在原始信号上的噪声。因此,借助信号去噪的方法,提出了一种基于SVM的共谋攻击优化算法。该算法利用含有指纹和不含指纹的数字作品对基于SVM的分类器进行训练,并获得最优的分类参数。接着,通过分类器计算利用不同小波包最优基获得的攻击图像到分类超平面的距离,从而选择最优的小波包最优基对作品进行去噪,实现移除嵌入在作品中的数字指纹的目的。
理论和实验结果表明,本文提出的三种共谋攻击优化算法优于常见的线性和非线性共谋攻击以及梯度攻击。然而,本文的研究基于检测器已知的假设,假如检测器未知时,如何设计通用且有效的共谋攻击优化算法,使得利用尽可能少的指纹样本便可以移除作品中的指纹,同时保持作品良好的视觉质量,需要开展进一步的研究与探讨。
With the rapid development of multimedia and Internet, especially the emergence of various of social network services, the multimedia data has been increasing explosively. This brings people a lot of convenience to share and get multimedia content from Internet. However, these benefits also bring ease to unauthorized use of multimedia content, such as illegal duplication, processing and redistribution, which leads to the copyright infringement and privacy leakage. Thus, the most essential problem is how to protect the copyright of multimedia data and how to prevent the illegal usage and dispersion.
Digital fingerprinting is considered as an efficient and most potential method to solve the aforementioned problem. Unique marks, known as fingerprints, are embedded in the content which is used to identify adversaries who leak the copies of the same content. How-ever, collusion attack is known to be a cost-effective attack and poses a great threat to the digital fingerprinting systems. In collusion attack, a group of users combines multiple copies of the same multimedia content to generate a new version. With enough number of col-lected copies, the adversaries arc able to attenuate or remove the fingerprints, which makes the detector unable to trace any of the real colluders involved. Therefore, understanding the weaknesses and the limitations of existing fingerprinting schemes and designing effective collusion attacks play an important role in the development of digital fingerprinting.
At first, we focus on studying the typical digital fingerprinting schemes and commonly used collusion attacks, and construct an general evaluation framework based upon the se-curity analysis of digital fingerprinting. Under this framework, we can evaluate the perfor-mance of typical digital fingerprinting schemes when suffering frequently used linear and non-linear collusion attacks.
Then, we propose there novel collusion attack optimization algorithms from three dif-ferent perspectives. At first, combining with the geometric representation of Voronoi dia-gram, we propose an Iterative Optimization Collusion Attack (IOCA). By using the iterative optimization method, we remove the fingerprint which can be detected as containing the col- luders in the Voronoi diagram, after processing of several iterations, to a signal which can be detected as not. After that, we evaluate the performance of the proposed collusion attack strategy in defeating four typical fingerprinting schemes under a well-constructed evaluation framework.
Although the IOCA is more effective in defeating the fingerprinting system than the traditional collusion attacks, it also introduces larger noticeable distortion. To improve the perceptual quality of the attacked signal, we propose a self-adaptive noising optimization (SANO) collusion attack based on the fact that the probability of the detector to be easily distorted by the noise. We at first analyze the influence of the Gaussian noise on both the detection probability of the correlation detector and the perceptual quality of the attacked content. Then, based upon the principle of the closed-loop feedback control theory, an adap-tive dynamic iterative approach is applied to add the noise to the forgery until obtaining a copy where the fingerprint is not presented or making the detector difficult to detect any of the pirates. The experimental results show that the proposed algorithm can efficiently inter-rupt the fingerprinting system with less than three independent copies of the same content.
From the perspective of the fingerprint embedding, we know that the fingerprint is in reality a noise signal added to the original content. Therefore, we propose a support vector machine (SVM)-based anti-forensic method by employing denoising approach. We at first train the SVM-based classifier using fingerprinted and un-fingerprinted copies, and obtain the optimal classification parameters. After that, we select the best basis through wavelet packet decomposition for thresholding the fingerprints from the forgery.
Theory analysis and experimental results illustrate that the proposed collusion attack optimization algorithms are more effective than usually used linear and nonlinear collusion attacks and the gradient attack. However, we assume that the fingerprinting extracting algorithm is known to the colluders in the proposed algorithms. It is worthwhile to explore an effective collusion attack strategy under the assumption that the fingerprinting extracting algorithm is unknown.
数字指纹技术是解决这类问题的一种有效和极具潜力的技术。它通过向待分发的多媒体作品中嵌入唯一性标识(即指纹),实现从非法的可疑拷贝中追踪到原始购买者的目的。然而,共谋攻击是数字指纹系统面临的主要威胁。在共谋攻击中,多个共谋者通过合并各自的指纹样本以便削弱或移除其中的数字指纹,从而达到逃避追踪的目的。因此,研究新的共谋攻击算法,对理解当前的数字指纹技术所存在的缺陷以便找到正确的应对方案具有重要意义。
首先,以经典的数字指纹算法及常见的共谋攻击算法为研究对象,在对数字指纹系统安全性分析的基础上,建立了一个通用的数字指纹安全分析与评测框架。在此框架之下,可以对各种数字指纹算法在抵抗常见的线性和非线性共谋攻击时的性能进行全面的分析与评测。
然后,以共谋攻击作为研究重点,从三个不同的角度考虑,提出了三种新的共谋攻击优化算法。首先结合Voronoi图的几何表示,提出了一种迭代优化共谋攻击算法。该算法利用迭代优化的方法,使得原本属于Voronoi图中某个检测区域的指纹信号,经过多次迭代之后,最终落在无指纹存在的检测区域。其次,以四种经典的数字指纹算法作为攻击目标对提出的迭代优化共谋攻击算法的攻击性能和攻击之后作品的视觉质量进行了实验比较与分析。
尽管迭代优化共谋攻击可以有效的移除作品中的指纹,但是也给攻击者之后的作品带来了较大的视觉失真。为了提高攻击之后作品的视觉质量,基于添加噪声可以干扰指纹检测的事实,提出了一种自适应噪声优化共谋攻击。该算法从理论上分析了添加到指纹样本中的不同能量的高斯噪声对指纹检测器的性能以及样本视觉质量的影响。然后,以闭环反馈控制原理为指导,通过自适应的动态优化方法不断的向平均之后指纹作品中添加噪声,直到获得没有指纹的作品。实验结果表明,该算法仅需要少个三个嵌有不同指纹的作品便可以完全的移除所有共谋者的指纹。
从指纹嵌入的角度来看,嵌入在作品中的数字指纹实际上相当于叠加在原始信号上的噪声。因此,借助信号去噪的方法,提出了一种基于SVM的共谋攻击优化算法。该算法利用含有指纹和不含指纹的数字作品对基于SVM的分类器进行训练,并获得最优的分类参数。接着,通过分类器计算利用不同小波包最优基获得的攻击图像到分类超平面的距离,从而选择最优的小波包最优基对作品进行去噪,实现移除嵌入在作品中的数字指纹的目的。
理论和实验结果表明,本文提出的三种共谋攻击优化算法优于常见的线性和非线性共谋攻击以及梯度攻击。然而,本文的研究基于检测器已知的假设,假如检测器未知时,如何设计通用且有效的共谋攻击优化算法,使得利用尽可能少的指纹样本便可以移除作品中的指纹,同时保持作品良好的视觉质量,需要开展进一步的研究与探讨。
With the rapid development of multimedia and Internet, especially the emergence of various of social network services, the multimedia data has been increasing explosively. This brings people a lot of convenience to share and get multimedia content from Internet. However, these benefits also bring ease to unauthorized use of multimedia content, such as illegal duplication, processing and redistribution, which leads to the copyright infringement and privacy leakage. Thus, the most essential problem is how to protect the copyright of multimedia data and how to prevent the illegal usage and dispersion.
Digital fingerprinting is considered as an efficient and most potential method to solve the aforementioned problem. Unique marks, known as fingerprints, are embedded in the content which is used to identify adversaries who leak the copies of the same content. How-ever, collusion attack is known to be a cost-effective attack and poses a great threat to the digital fingerprinting systems. In collusion attack, a group of users combines multiple copies of the same multimedia content to generate a new version. With enough number of col-lected copies, the adversaries arc able to attenuate or remove the fingerprints, which makes the detector unable to trace any of the real colluders involved. Therefore, understanding the weaknesses and the limitations of existing fingerprinting schemes and designing effective collusion attacks play an important role in the development of digital fingerprinting.
At first, we focus on studying the typical digital fingerprinting schemes and commonly used collusion attacks, and construct an general evaluation framework based upon the se-curity analysis of digital fingerprinting. Under this framework, we can evaluate the perfor-mance of typical digital fingerprinting schemes when suffering frequently used linear and non-linear collusion attacks.
Then, we propose there novel collusion attack optimization algorithms from three dif-ferent perspectives. At first, combining with the geometric representation of Voronoi dia-gram, we propose an Iterative Optimization Collusion Attack (IOCA). By using the iterative optimization method, we remove the fingerprint which can be detected as containing the col- luders in the Voronoi diagram, after processing of several iterations, to a signal which can be detected as not. After that, we evaluate the performance of the proposed collusion attack strategy in defeating four typical fingerprinting schemes under a well-constructed evaluation framework.
Although the IOCA is more effective in defeating the fingerprinting system than the traditional collusion attacks, it also introduces larger noticeable distortion. To improve the perceptual quality of the attacked signal, we propose a self-adaptive noising optimization (SANO) collusion attack based on the fact that the probability of the detector to be easily distorted by the noise. We at first analyze the influence of the Gaussian noise on both the detection probability of the correlation detector and the perceptual quality of the attacked content. Then, based upon the principle of the closed-loop feedback control theory, an adap-tive dynamic iterative approach is applied to add the noise to the forgery until obtaining a copy where the fingerprint is not presented or making the detector difficult to detect any of the pirates. The experimental results show that the proposed algorithm can efficiently inter-rupt the fingerprinting system with less than three independent copies of the same content.
From the perspective of the fingerprint embedding, we know that the fingerprint is in reality a noise signal added to the original content. Therefore, we propose a support vector machine (SVM)-based anti-forensic method by employing denoising approach. We at first train the SVM-based classifier using fingerprinted and un-fingerprinted copies, and obtain the optimal classification parameters. After that, we select the best basis through wavelet packet decomposition for thresholding the fingerprints from the forgery.
Theory analysis and experimental results illustrate that the proposed collusion attack optimization algorithms are more effective than usually used linear and nonlinear collusion attacks and the gradient attack. However, we assume that the fingerprinting extracting algorithm is known to the colluders in the proposed algorithms. It is worthwhile to explore an effective collusion attack strategy under the assumption that the fingerprinting extracting algorithm is unknown.
引文
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