【作者】 王伟；

【导师】 方勇；

【作者基本信息】 上海大学 ， 通信与信息系统， 2011， 博士

【摘要】 盲信号分离是只根据观测到的信号来分离或恢复出未知源信号的过程,是近年来信号处理领域和神经网络领域研究的热点之一,在图像处理,语音识别等方面有着许多潜在的应用。目前,人们在盲源分离方面做了很多有益的研究,根据观测信号与源信号的数目关系,可分为超定、适定、欠定以及单通道盲分离,其中以单通道盲分离最具有挑战性。以往研究者研究的单通道盲分离问题大都假设多个源信号之间是以叠加的方式进行混叠的,然而在实际应用中,可能观测到的一类混合信号不是以叠加方式,而是以置换的方式与另外的信号发生混叠,由于这两种混合方式完全不同,现有的盲分离方法就不能用来解决这种单通道的置换混叠信号的分离问题。本文围绕置换混叠图像的盲分离问题展开研究,并将这种置换盲分离思想引入到图像篡改检测研究中,为目前广泛研究的图像被动取证提供新的解决方案。论文的主要工作概括如下:1、首次对单通道的置换混叠图像盲分离问题展开深入研究。根据置换混叠图像的定义给出了其数学模型,并在置换混合矩阵为特殊二值矩阵(0、1矩阵)的基础上,提出了基于混合矩阵估计的置换混叠盲分离模型。同时,针对不同的源图像,我们提出了一系列基于参数估计的单通道盲分离方法。2、在源信号为JPEG图像的前提下,针对JPEG置换混叠图像盲分离问题,提出了基于压缩因子估计的盲分离方法。该方法利用JPEG压缩子块被相同压缩因子再次压缩前后基本保持不变的特性,根据再次压缩前后图像子块的相关性大小估计出原始压缩因子,并以此构造一个映射空间,在映射空间利用压缩因子参数的不同分类实现了置换混合矩阵的估计,从而实现了源图像子块的分离。最后,将该盲分离方法应用到JPEG篡改图像的盲检测中,仿真结果表明该方法的有效性和鲁棒性。3、在源信号为模糊图像的条件下,利用模糊置换混叠图像的频域特性估计出模糊参数,提出了两种基于盲复原的盲分离方法。对于散焦模糊置换混叠图像,采用对角线分析方法来估计散焦模糊半径R ,再用Lucy-Richardson(L-R)算法对置换混叠图像进行盲复原,并通过定义像素梯度绝对值和来对复原产生的振铃效应进行评价,根据评价结果进行分类估计出置换混合矩阵,从而完成置换源图像子块的分离。对于运动模糊置换混叠图像,则利用Radon变换和极值检测估计出点扩展函数的运动方向和长度,再利用L-R算法对置换图像进行盲复原,然后,根据对复原产生的振铃效应进行评价估计出置换混合矩阵,进而恢复出源图像子块。最后,将上述两种基于模糊参数估计的盲分离方法应用到图像的被动取证中,实验结果表明篡改区域均能被准确的检测定位出来。4、在假定置换混叠图像中包含插值图像的基础上,提出了一种插值因子估计算法来实现插值放大置换混叠图像的盲分离。该方法利用有限差分算法来对插值引入的周期性进行检测,根据周期特性的异同估计出原始插值因子,并根据估计出的插值因子构造一个映射空间,在映射空间利用插值因子参数的可分性实现了置换混合矩阵的估计,从而实现源图像的分离。最后,利用基于插值因子估计的盲分离方法来实现插值放大篡改图像的盲检测,仿真结果表明了上述方法不但具有更好的噪声和JPEG压缩鲁棒性,而且具有更低的算法复杂度。5、在针对经过模糊处理后的置换混叠图像,提出一种基于二次模糊相关性的单通道盲分离方法。该方法通过对置换混叠图像进行二次模糊,估计二次模糊前后对应子块的相关系数来构造一个映射空间,利用映射空间内参数的不同分类完成置换混合矩阵的估计,从而实现置换源图像子块的分离。最后,将上述盲分离方法应用到经过模糊后处理的篡改图像盲检测中,文中实验结果表明上述方法对经历不同模糊后处理的篡改图像均能达到较高的检测正确率,同时,算法对高斯噪声和有损JPEG压缩都具有较好的鲁棒性。更多还原

【Abstract】 Blind sources separation (BSS), which became an active research topic in signal processing and neural network in the last decade, consists of separating a set of unknown signals from a set of linear mixtures of them, when no knowledge is available about the mixing coefficients. There are many potential exciting applications of blind sources separation in science and technology, especially in image processing and speech recognition. According to some hypotheses on the number of sources and the number of observed signals, BSS is divided into overdetermined blind sources separation (OBSS), welldetermined blind sources separation (WBSS), underdetermined blind sources separation (UBSS) and single channel blind signal separation (SCBSS). The SCBSS is comparing with multiple channel blind separation, which has more challenging. In the earlier research of the SCBSS, generally, most of the methods to SCBSS assume that the mixing mode of individual source signals is superposition. However, if the mixed mode is permutation, a quite different one from superposition, the approaches mentioned above will no longer be functional. In this dissertation, we investigate the BSS problem of the permuted image and introduce the thought into image tamper detection. The primary contributions of the dissertation are summarized blow:1. A first and in-depth study on the BSS problem of the permuted image. The mathematical model of the permuted image is obtained by its definition. According to the permutation mixing matrix is a special binary matrix, of which each entry equals 0 or 1, a BSS model of the permuted image is described by estimating the mixing matrix. For different source images, a series of single-channel blind separation algorithms are proposed based on parameters estimation.2. When the sources are JPEG images, a blind separation method based on compression factors estimation is proposed for permuted JPEG image. The permuted image is compressed again and the primary compression factors are estimated by calculating the correlation coefficients of image blocks with before and after recompression. Using the estimated compression factors, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in JPEG image tamper detection. Simulation results show the validity and robustness of the proposed algorithm.3. When the sources are blurred images, two novel single-channel blind separation algorithms for permuted blurred image are proposed by using blind restoration. For permuted defocus blurred image, the defocus blur radius is estimated by the characteristics of permuted image in the frequency domain, and then the permuted image is restored by performing the Lucy-Richardson(L-R)blind restoration method. The ringing effect of restored image is measured by defining the sum of absolute pixel gradient, and the permutation mixing matrixes can be accurately estimated by classifying the ringing effect of each sub-block, thereby separating the source images. For permuted motion blurred image, both the motion direction and length of point spread function (PSF) are estimated by Radon transformation and extrema detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrixes can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. At last, two proposed algorithms are applied to image passive forensics. Simulation results show the tamper regions can be accurately detected.4. When the sources contain interpolated images, a novel blind separation method based on interpolation factors estimation is proposed for permuted interpolated image. The periodic property of difference sequence is detected by finite-difference for permuted image, according to the periodic property, the various interpolation factor could be identified. Using the estimated interpolation factors, a mapping space is constructed. The permutation mixing matrixes can be estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed method is used in interpolation image tamper detection. Simulation results show the validity and robustness of the proposed algorithm. Compared with existing ones, the algorithm has simple principle and small computational load.5. For blurring permuted images, a single-channel blind separation scheme using double blur correlation is proposed. The permuted image is blurred again and the correlation coefficients of image blocks are estimated before and after double blurring. Using the estimated correlation coefficients, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in the mapping space, thus the source images can be separated. At last, the proposed algorithm is applied to image tamper detection with blurring. Simulation results show high detection accuracy for tamper images with various blurring operations. The proposed method has good robustness against Gaussian noise and lossy JPEG compression.更多还原