图像模糊检测方法

针对数字伪造图像中最常用的模糊操作手段之一的高斯模糊操作,文中提出了一种基于同态滤波和DCT功率谱的图像模糊检测方法.该方法分析了离焦模糊和人工模糊的边界特点,首先利用同态滤波增强人工模糊操作的边缘,并提取增强后图像的二值边缘,从而区分自然模糊和人工模糊,以减小实验中出现的误判,再利用图像的DCT功率谱的分布检测出模糊篡改的区域.该方法能够有效地检测出图像的模糊操作,并进行准确定位.     

Joint image splicing detection in DCT and Contourlet transform domain

Splicing is a fundamental and popular image forgery method and image splicing detection is urgently called for digital image forensics recently. In this paper, a Markov based approach is proposed to detect image splicing. The paper applies the Markov model in the block discrete cosine transform (DCT) domain and the Contourlet transform domain. First, the original Markov features of the inter-block between block DCT coefficients are improved by considering the different frequency ranges of each block DCT coefficients. Then, additional features are extracted in Contourlet transform domain to characterize the dependency of positions among Contourlet subband coefficients. And these features are extracted from single color channel for gray image while extracted from three color channels for color image. Finally, Support Vector Machines (SVMs) are exploited to classify the authentic and spliced images for the gray image dataset while ensemble classifier to the color image dataset. The experiment results demonstrate that the proposed detection scheme outperforms some state-of-the-art methods when applied to Columbia Image Splicing Detection Evaluation Dataset (DVMM), and ranks fourth in phase 1 on the Live Ranking of the first Image Forensics Challenge.     

Copy-move image forgery detection based on Gabor magnitude

With advancement of media editing software, even people who are not image processing experts can easily alter digital images. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this paper, we present an efficient and robust method to detect such artifacts. First, the tampered image is segmented into overlapping fixed-size blocks, and the Gabor filter is applied to each block. Thus, the image of Gabor magnitude represents each block. Secondly, statistical features are extracted from the histogram of orientated Gabor magnitude (HOGM) of overlapping blocks, and reduced features are generated for similarity measurement. Finally, feature vectors are sorted lexicographically, and duplicated image blocks are identified by finding similarity block pairs after suitable post-processing. To enhance the algorithm’s robustness, a few parameters are proposed for removing the wrong similar blocks. Experiment results demonstrate the ability of the proposed method to detect multiple examples of copy-move forgery and precisely locate the duplicated regions, even when dealing with images distorted by slight rotation and scaling, JPEG compression, blurring, and brightness adjustment.     

半色调图像中数据隐藏算法

在许多打印和出版应用中,需要在半色调图像中嵌入数据用于作品的版权保护和认证.本文提出了一种半色调图像中数据隐藏的算法,该算法利用DCT的中频系数比较法进行数字水印信息的嵌入.在对数字水印信息进行嵌入前,首先进行编码,转化成比特流,然后通过改变DCT中频系数的序关系嵌入数字水印,经打印/扫描后提取出水印,实验结果显示:该算法能够抵抗打印—扫描攻击,并且隐藏效果好,恢复水印时不需要原始图像.该算法也可用于普通证件的防伪.     

Copy-move tampering detection using affine transformation property preservation on clustered keypoints

Recent advances in multimedia technologies have made imaging devices and image editing tools ubiquitous and affordable. Image editing done with malicious intent is called as image tampering or forgery. The most common forgery is the copy-move forgery which involves copying a part of an image and pasting it on some other part of the same image. There are many existing methods for such forgery detection, but most of them are sensitive to post-processing and do not detect multiple instances of forgeries in an image. In the proposed approach, affine transformation property preservation of clustered keypoints in the image is used, which includes the tests for collinearity and distance ratio preservation. Our method is also able to detect multiple copy-move forgeries within an image. The proposed method is tested against four image tampering detection datasets, and the results of our method are the best compared to the existing eight state-of-the-art methods in terms of accuracy.     

An Efficient Image Forgery Detection Using Biorthogonal Wavelet Transform and Improved Relevance Vector Machine

Nowadays, the development of refined image processing and software editing tools has finish the exploitation of digital images easily and invisible the image to the normal eyes and this process known as image fakery. Image security is one of the key issues in any field that makes use of digital images. Copy-move forgery (CMF) is the most effective and simple scheme to create forged digital images. In general, the methodologies based on Scale Invariant Feature Transform (SIFT) are widely used to detect CMF. Unfortunately, the detection performance of all SIFT based CMF detection approaches are extremely dependent on the selection of feature vectors. The values of these parameters are often determined through experience or some experiments on a number of forgery images. However, these experience parameter values are not applicable to every image thereby offers a limited usefulness. This paper deals the CMF problem using improved Relevance Vector Machine technique. The key idea of the IVRM is to apply Biorthogonal Wavelet Transform based scheme on image for feature extraction. The feature vectors are then stored lexicographically and similarity of vectors is decided using Minkowski distance and threshold value. The simulation results of proposed technique show a significant improvement in accuracy, sensitivity, and specificity rates over others existing schemes.     

区分来源和目标区域的图像copy-move伪造检测方法

Copy-move是一种常用的图像伪造手段,它通过复制图像的某一区域,移动并粘贴到同一图像的其他位置,达到掩盖重要信息或伪造虚假场景的目的。近年来,为了防止copy-move被用于违法犯罪,copy-move伪造检测技术迅猛发展,在维护社会运行秩序和信息安全方面发挥着积极作用。本文提出一种基于条件生成对抗网络(conditional Generative Adversarial Networks, cGANs)的copy-move伪造检测方法。针对图像copy-move伪造检测,该方法优化设计了cGANs的损失函数,并使用适量的弱监督样本来提升网络性能。不同于目前大部分检测算法,该方法不仅可以定位出图像中的相似区域,还可以有效区分伪造来源区域和伪造目标区域。实验结果表明,本文所提出的方法在检测准确率上显著优于现有方法。      

图像空域分析下的图像篡改检测算法

提出一种图像空域分析下的图像篡改检测算法。首先,利用GPAC分割出相似性区域;然后,利用双边滤波对相似性区域的灰度图进行去噪,并得到其噪声图像;最后,通过比较噪声图像的灰度值分布是否相同或噪声分布规律相似性程度来判断图像的真伪,并判断出其篡改方式。通过实验证明该算法能有效的检测出篡改图像。     

数字图像复制粘贴篡改盲取证算法研究与实现

针对数字图像检测中一类常见的复制－粘贴图像篡改,提出了一种基于小波变换和奇异值分解的检测算法。该算法利用小波变换提取图像的低频分量,对低频分量分块提取奇异值特征,然后将特征矢量进行按行字典排序,并且配合图像块的偏移位置信息,进行图像复制伪造区域的检测和定位。实验表明该算法大大减小了特征向量的维数,从而提高了相似块的匹配检测效率。为了更方便快捷的检测图像是否被恶意篡改,设计了简单明了的系统检测界面,只需载入待检测的图像并输入相应的参数就能进行检测,最后将检测结果返回给界面,而且系统完成了篡改检测算法的DSP硬件实现,该算法将有利于推动数字图像取证技术的理论研究与应用推广的发展。     

基于图像背景噪声特性的篡改检测

数字图像都包含有一部分来自成像过程或者数字压缩的背景噪声,如果两幅不同背景噪声的图像被拼接在一起,则图像篡改区域和其他区域的噪声特性会有差异。本文基于一种估计信道信噪比的高阶统计量法提出了一种新的图像背景噪声的盲估计算法。通过对图像进行分块计算每块的噪声方差,从而检测图像篡改部分。此算法通过二次加噪的方法解决了高阶统计量法中必须已知原始信号的问题,实现了待检测图像噪声的盲估计。实验结果显示该算法能有效估计图像的噪声方差从而达到检测局部篡改的目的。并且图像的缩放和压缩对检测结果影响很小,算法具有较好的鲁棒性。      

数字水印盲检测算法自适应门限的确定

数字媒体的迅速发展使得其版权保护成为人们关注的焦点，从而使数字水印和信息隐藏技术日益凸显其重要的学术价值和应用前景。本文提出了一种新颖的基于离散余弦变换(DCT)的数字水印盲检测算法，并使用了概率论的方法对水印检测的门限进行了理论推导，使检测结果更具客观性。实验结果表明，该算法在图像数字水印方面是十分有效和实用的，并且对于绝大多数的攻击算法显示出极强的鲁棒性。     

Copy-move forgery detection and localization by means of robust clustering with J-Linkage

Understanding if a digital image is authentic or not, is a key purpose of image forensics. There are several different tampering attacks but, surely, one of the most common and immediate one is copy-move. A recent and effective approach for detecting copy-move forgeries is to use local visual features such as SIFT. In this kind of methods, SIFT matching is often followed by a clustering procedure to group keypoints that are spatially close. Often, this procedure could be unsatisfactory, in particular in those cases in which the copied patch contains pixels that are spatially very distant among them, and when the pasted area is near to the original source. In such cases, a better estimation of the cloned area is necessary in order to obtain an accurate forgery localization. In this paper a novel approach is presented for copy-move forgery detection and localization based on the J-Linkage algorithm, which performs a robust clustering in the space of the geometric transformation. Experimental results, carried out on different datasets, show that the proposed method outperforms other similar state-of-the-art techniques both in terms of copy-move forgery detection reliability and of precision in the manipulated patch localization.     

A Block-Level Image Tamper Detection Scheme Using Modulus Function Based Fragile Watermarking

Intentional illegitimate modification in any digital image data is one of the general malversations in the existing digital domain. Therefore in this work, the authors have devised a fragile watermarking technique for the localization of illegitimate modifications in the digital image content effectually. The proposed technique detects forged digital image content strongly in the block-level of two successive pixels. This scheme is performed at the block level, where the actual image is partitioned into non-overlapping blocks of size (1?×?2) pixels. Afterward, the block-level authentication code is generated from the MSB of two successive pixels of each block using averaging and modulus operations. The generated authentication code/watermark is encrypted using a logistic-map-based chaotic key series. The encrypted authentication code is embedded into two successive pixels of the corresponding block. Further, any form of forgery in the watermarked image can be detected by comparing its extracted authentication code and regenerated authentication code. The proposed procedure is successfully experienced on a variety of grayscale images, and the experimental results exhibit that watermarked images generated by this scheme are of considerably high quality in terms of PSNR, IF, and SSIM. The proposed method is capable of efficient forgery detection by achieving very high accuracy, NC, and true positive rate while maintaining considerably low false-positive and false-negative rates.

     

图像镜像复制粘贴篡改检测中的FI-SURF算法

针对数字图像版权中的复制粘贴篡改问题,提出FI-SURF (flip invariant SURF)算法。研究了当图像经过镜像翻转后SURF (speeded-up robust features)特征描述符的排列变化关系。提取SURF特征点后,将其特征描述符重新排序,即使复制粘贴区域经过镜像翻转,对应的特征点依然可以进行匹配。实验证明,FI-SURF算法在保留SURF算法运算速度快、顽健性强等优点的前提下,可有效检测出经过镜像翻转的复制粘贴区域,计算出复制粘贴区域的轮廓。     

基于DCT系数双量化映射关系的图像盲取证算法

图像篡改过程会留下一些痕迹破坏自然图像的一致性,为图像的盲取证提供了线索。该文针对篡改过程中的JPEG重压缩操作,根据原始离散余弦变换(DCT)系数与重压缩后DCT系数的映射关系,提出一种新的图像重压缩概率模型来描述重压缩前后DCT系数统计特性的变化,并结合贝叶斯准则,利用后验概率表示JPEG篡改图像中存在的 DQ(Double Quantization)效应,通过后验概率密度图实现篡改区域的定位。实验表明,该方法能够快速并准确实现篡改区域的自动检测和定位,尤其是当第2次压缩因子小于第1次压缩因子时,正确率相对于传统算法有明显的提高。该方法不仅能检测Photoshop等图像编辑软件制作的手工合成篡改图像,同样也适用于图像智能编辑算法如图像修复算法和图像重排算法制作的篡改图像。     

Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of interpolation

Availability of the powerful image editing softwares and advancement in digital cameras has given rise to large amount of manipulated images without any traces of tampering, generating a great demand for automatic forgery detection algorithms in order to determine its authenticity. When altering an image like copy–paste or splicing to conceal traces of tampering, it is often necessary to resize the pasted portion of the image. The resampling operation may highly likely disturb the underlying inconsistency of the pasted portion that can be used to detect the forgery. In this paper, an algorithm is presented that blindly detects global rescaling operation and estimate the rescaling factor based on the autocovariance sequence of zero-crossings of second difference of the tampered image. Experimental results using UCID and USC-SIPI database show the validity of the algorithm under different interpolation schemes. The technique is robust and successfully detects rescaling operation for images that have been subjected to various forms of attacks like JPEG compression and arbitrary cropping. As expected, some degradation in detection accuracy is observed as the JPEG quality factor decreased.     

基于扩展马尔科夫特征的Seam-Carving篡改检测

针对能够用于图像篡改的Seam-Carving技术,提出了一种基于扩展的马尔科夫特征的Seam-Carving篡改识别算法。该算法充分考虑了Seam-Carving操作导致的图像频域特征的变化,将传统的利用马尔科夫转移概率矩阵求取的图像特征和基于扩展的马尔科夫转移概率特征进行融合,而后利用支持向量机进行分类训练,从而达到有效识别基于Seam-Carving的图像篡改。实验结果表明,提出的方案性能优于传统的基于马尔科夫转移矩阵的特征选择方法以及现有的一些该类图像篡改检测方法。     

基于融合DCT和LBP特征提取的面部表情识别

针对离散余弦变换(DCT)只能提取面部表情图像的全局特征,而忽略了临近像素之间的关系、不能提取纹理特征信息、不能准确区分相似表情等问题,提出一种融合离散余弦变换方法和局部二值模式(LBP)特征的表情特征提取方法。该方法首先将人脸图像经过DCT获得的低频系数作为表情的全局特征;然后用LBP对贡献率较大的嘴部、眼睛区域进行局部纹理特征提取,通过将LBP提取到的局部纹理特征与DCT提取到的全局特征进行融合,从而得到更有效的表情特征;最后利用支持向量机(SVM)进行识别。实验结果表明:该方法比单独使用DCT方法提取的表情特征更有利于识别,提高了表情识别的准确性,并将这个表情识别方法用于智能轮椅的控制上,收到了良好的效果。     

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.     

基于极性复指数变换的图像伪造检测算法

为解决当前图像伪造检测方法在识别复制内容区域时忽略了颜色信息和不同颜色分量之间的相关性,使其对伪造内容的定位与检测准确度不理想的问题,设计了基于改进的加速稳健特征(SURF)描述符与多元极性复指数变换的图像伪造检测算法。引入高斯低通滤波器,对彩色图像完成过滤,以消除噪声,再计算滤波图像的颜色不变性,用其替代SURF描述符中的灰度分量,对SURF方法予以改进,获取新的Hessian矩阵,充分检测彩色图像中的兴趣点;随后,利用这些兴趣点来构建一组连通的Delaunay三角网。基于四元极性复指数变换,充分考虑不同颜色分量之间的相关性,有效提取三角网的局部视觉特征;计算视觉特征之间的欧式距离,根据预设阈值,对三角网实施配准;最后,引入随机样本一致性,剔除错误匹配的三角网,并定义后处理方法,检测出复制伪造区域。测试数据显示：相对已有的复制-粘贴伪造检测方法,在多种几何变换条件下,所提方法具有更高的伪造检测准确性。