An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.     

Matching images based on consistency graph and region adjacency graphs

The image matching methods based on regions have many advantages over the point matching techniques, and the most charming one is that once region being matched, all pixels are matched in theory. It would benefit many applications, such as object retrieval, stereo corresponding, semantic understanding a scene, object tracking. This paper proposes a new region matching algorithm based on consistency graph and region adjacency graphs. Firstly, the segmented images are transformed into region adjacency graphs, and the potential region pairs and the potential edge segment pairs are packaged in a consistency graph. Since the rightly matched pair always is accompanied by harmonious neighbourhoods, the right correspondences tend to cluster together, and the error corresponding relationship should have few chances to connect to any compatible neighbourhood. Thus, the solution space is greatly reduced and the corresponding relationship can be found in a polynomial computational complexity just by a simple method, such as seed-growth method. To the best of our knowledge, the method is the first one to match two images by region adjacency graphs and find the corresponding relationship in a polynomial computational complexity. Experiments on the existing benchmark show that the proposed method could quickly find the right corresponding relationship between images with illumination, rotation and affine transformation.     

基于SIFT特征的两阶段procrustes迭代匹配算法

以得到尽量准确的图像SIFT(Scale Invariant Feature Transform)特征点之间的匹配关系为目的,给出了一种基于SIFT特征的两阶段procrustes迭代匹配方法。该方法首先基于图像SIFT描述向量得到图像SIFT特征点之间的初始匹配关系,初始匹配的特征点之间存在较多的错误匹配特征点对,然后利用特征点之间全局的几何约束采用第一阶段的procrustes迭代匹配方法去除错误匹配的特征点对,这时部分正确的匹配特征点对也可能被去除,最后利用另一个procrustes迭代匹配过程找回被去除的正确匹配特征点对。仿真实验表明,本文的方法有效去除了SIFT描述向量匹配中存在的错误匹配点对,并能找回被去除的正确匹配点对,得到图像特征点之间正确的匹配关系。      

一种基于特征交汇关键点检测和Sim-CSPNet的SAR图像配准算法

合成孔径雷达(SAR)图像存在固有的相干斑噪声和几何畸变,并且其成像过程中图像之间存在非线性辐射差异,因此SAR图像配准是近年来最具挑战性的任务之一。关键点的可重复性和特征描述符的有效性直接影响基于特征的配准方法精度。该文提出了一种新颖的基于特征交汇的关键点检测器,它包含3个并行的检测器,即相位一致性(PC)检测器、水平和垂直方向梯度检测器以及局部变异系数检测器。所提出的特征交汇关键点检测器不仅可以有效提取具有高重复性的关键点,而且大大减少了错误关键点的数量,从而降低了特征描述和匹配的计算成本。同时,该文设计了一种孪生跨阶段部分网络(Sim-CSPNet)来快速提取包含深层和浅层特征的特征描述符。与传统手工设计的浅层描述符相比,它可以用来获得更准确的匹配点对。通过对多组SAR图像进行配准实验,并与其他3种方法进行对比,验证了该方法具有很好的配准结果。      

基于相似三角形匹配的红外与可见光图像配准方法

提出了一种基于相似三角形匹配的红外与可见光图像配准方法:首先将提取出的Harris角点组成三角形,然后利用本文提出的搜索算法,从红外与可见光图像中找出一对最优的相似三角形,并利用相似三角形的性质,计算出相关的匹配点,最后通过RANSAC算法拟合出配准参数,对图像进行配准。实验结果证明:此算法与传统的基于特征点的图像配准方法相比精度更高,具有较强的鲁棒性,可以解决刚体变换下红外与可见光图像配准的问题。     

Robust semi-blind video watermarking based on frame-patch matching

In this paper, we present a frame-patch matching based robust semi-blind video watermarking using KAZE feature. The KAZE feature is employed for matching the feature points of frame-patch with those of all frames in video for detecting the embedding and extracting regions. In our method, the watermark information is embedded in Discrete Cosine Transform (DCT) domain of randomly generated blocks in the matched region. In the extraction process, we synchronize the embedded region from the distorted video by using KAZE feature matching. Based on the matched KAZE feature points, RST (rotation, scaling, translation) parameters are estimated and the watermark information can be successfully extracted. Experimental results show that our proposed method is robust against geometrical attacks, video processing attacks, temporal attacks, and so on.     

Matching 3-D prone and supine CT colonography scans using graphs

In this paper, we propose a new registration method for prone and supine computed tomographic colonography scans using graph matching. We formulate 3-D colon registration as a graph matching problem and propose a new graph matching algorithm based on mean field theory. In the proposed algorithm, we solve the matching problem in an iterative way. In each step, we use mean field theory to find the matched pair of nodes with highest probability. During iterative optimization, one-to-one matching constraints are added to the system in a step-by-step approach. Prominent matching pairs found in previous iterations are used to guide subsequent mean field calculations. The proposed method was found to have the best performance with smallest standard deviation compared with two other baseline algorithms called the normalized distance along the colon centerline (NDACC) ( p = 0.17) with manual colon centerline correction and spectral matching ( p < 1e-5). A major advantage of the proposed method is that it is fully automatic and does not require defining a colon centerline for registration. For the latter NDACC method, user interaction is almost always needed for identifying the colon centerlines.     

基于Radon和解析Fourier-Mellin变换的篡改图像盲检测算法

复制粘贴（Copy-Move）是一种极为常见的图像篡改方式。为了快速有效地检测图像经过旋转、缩放等操作后的篡改图像,本文提出了一种基于Radon和解析Fourier-Mellin变换的篡改图像盲检测方法。文章首先对图像进行分块,之后将图像块进行Radon和解析Fourier-Mellin变换,并提取计算变换结果后的矩特征值,最后计算矩特征值的相关性。本文算法不需要对灰度图像进行二值化与归一化处理,而是直接从图形的Radon变换与Fourier-Mellin变换的结果中提取不变特征,理论分析与实验结果表明,本文提出算法的检测结果优于基于正交矩的检测方法,而且对均值为0的白噪声的鲁棒性显著高于基于正交矩的检测方法。      

基于引导滤波及视差图融合的立体匹配

立体匹配是双目视觉领域的重要研究方向.为在保证图片纹理区域匹配精度的同时降低弱纹理区域的误匹配率,提出一种基于引导滤波及视差图融合的立体匹配方法.首先,根据图像颜色相似性将图片划分为纹理较丰富区域和弱纹理区域.接着,分别采用不同参数的引导滤波进行代价聚合及视差计算,得到两张视差图.然后依据纹理区域划分的结果对获得的两张视差图进行融合.最后,通过左右一致性检测、加权中值滤波等视差优化步骤得到最终视差图.对Middlebury测试平台上标准图像对的实验结果表明,该方法在6组弱纹理图像上的平均误匹配率为9.67％,较传统引导滤波立体匹配算法具有更高的匹配精度.     

全景图像自动拼接算法的优化设计

提出了一种结合空域和频域进行全景图像序列自动拼接融合的优化算法。首先利用改进的相位相关法对全景序列图像进行自动排序并确定重叠区域,根据重叠区域像素均值进行图像整体亮度差异自动调整以降低角点的误匹配率;然后使用改进的Harris 算子在空间域提取图像角点（无需人工设定阈值）,通过双向最大互相关系数匹配获得初始特征点对,并用RANSAC 算法实现精确匹配;最后采用非线性平滑算法对图像重叠区域进行融合处理。实验结果表明,该优化算法排序过程简单有效,特征点提取匹配过程的成功率和效率都较现有算法有很大提高,拼接的图像清晰度高,具有较高的稳健性和拼接精度。     

Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection

Near-duplicate (ND) detection appears as a timely issue recently, being regarded as a powerful tool for various emerging applications. In the Web 2.0 environment particularly, the identification of near-duplicates enables the tasks such as copyright enforcement, news topic tracking, image and video search. In this paper, we describe an algorithm, namely Scale-Rotation invariant Pattern Entropy (SR-PE), for the detection of near-duplicates in large-scale video corpus. SR-PE is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints. More importantly, the coherency of patterns and the perception of visual similarity, under the scenario that there could be multiple ND regions undergone arbitrary transformations, respectively, are carefully addressed through entropy measure. To demonstrate our work in large-scale dataset, a practical framework composed of three components: bag-of-words representation, local keypoint matching and SR-PE evaluation, is also proposed for the rapid detection of near-duplicates.     

Image matching using enclosed region detector

Affine-invariant region detection is the basic technique for visual matching and has been widely applied in many areas. In this paper, we propose a simple yet effective method to detect the affine-invariant regions from gray image, which is called enclosed region. The enclosed region is detected based on the observation that one physical object is enclosed by the same region before and after affine transformation. The proposed method is a three-step method. Firstly, we segment the initial regions by using thresholds on the image. Secondly, external enclosing region (EER) and internal enclosed region (IER) are defined for each initial region, and we select the enclosed regions from the initial regions through applying histogram constraints on EER and IER. Thirdly, the largely overlapping regions are removed. Experiments on typical images exhibit the robustness of the proposed enclosed region detector. Extensively quantitative evaluation and comparison demonstrate that the proposed method outperforms state-of-the-art methods.     

基于局部子区域的活动轮廓图像分割方法

这里的方法旨在解决复杂多纹理图像中的目标分割问题。沿袭局部化思路,进一步提出基于局部子区域的方法,介绍了该方法下能量函数的设计方法,并与全局统一能量模型方法和局部能量方法进行了实验对比。实验表明,该方法在继承一般局部区域方法的分割能力的同时,较好的解决了复杂多纹理图像的目标分割问题,能较好的分割多纹理形成的物体边界,成功扩展了基于区域的活动轮廓的分割方法对图像的适应能力。     

Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.     

一种远程动中通卫星天线的设计与实现

卫星移动通信系统具有覆盖区域大、通信距离远、通信机动灵活等优点。卫星移动通信为远程动中通技术的发展、实现全球的无缝连接提供了契机。介绍了一种高性能、低轮廓Ku频段远程动中通卫星天线的工作原理,提出了动中通天线空间维和功能维概念,应用了喇叭天线阵波导馈电技术,给出了动中通天线设计与集成方法,分析了动中通天线可靠跟踪的关键技术并进行了设计实现。测试结果表明天线性能优良。     

基于SIFT的图像配准方法

针对大尺度图像配准和不同传感器图像配准问题,介绍了一种基于SIFT的图像配准方法。首先提取图像中适应尺度变化的不变特征点,在提取过程中加入多尺度Harris检测算子,提高了匹配点对的重复率,通过聚类和归一化互信息准则对候选匹配点对的角度、尺度和位置特征进行迭代筛选,删除错误的匹配点对,最后得到正确的匹配点对,对图像进行配准。实验结果表明：该方法能处理相似变换的图像配准。