Detecting copy-move forgery under affine transforms for image forensics

In copy-move forgery, the copied region may be rotated and/or scaled to fit the scene better. Most of the existing methods fail when the region is subject to affine transforms. This paper presents a method for detecting this kind of image tampering based on circular pattern matching. The image is first filtered and divided into circular blocks. A rotation and scaling invariant feature is then extracted from each block using Polar Harmonic Transform (PHT). The feature vectors are then lexicographically sorted, and the forged regions are detected by finding the similar block pairs after proper post-processing. Experimental results demonstrate the efficiency of the method.     

Copy-move forgery detection using adaptive keypoint filtering and iterative region merging

Multimedia Tools and Applications - Copy-move forgery detection can generally be divided into two categories: block-based or keypoint-based methods. However, the existing block-based methods are...     

A new keypoint-based copy-move forgery detection for small smooth regions

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.     

Accurate and robust localization of duplicated region in copy–move image forgery

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.     

鲁棒的区域复制图像篡改检测技术

区域复制把数字图像中一部分区域进行复制并粘贴到同一幅图像的另一个区域中,以达到去除图像中某一重要内容的目的,是一种简单而有效的图像篡改技术.现有检测算法对区域复制后处理的鲁棒性较差.文中针对此篡改技术,提出了一种有效的检测与定位篡改区域算法.该算法首先将图像分解为小块并比较各小块间的相似性,最后利用"主转移向量"方法去除错误的相似块对得到篡改的区域.实验数据说明该算法能有效地对抗多种区域复制的后处理操作,包括高斯模糊、加性白高斯噪声、JPEG压缩及它们的混合操作.     

PCET based copy-move forgery detection in images under geometric transforms

With the advent of the powerful editing software and sophisticated digital cameras, it is now possible to manipulate images. Copy-move is one of the most common methods for image manipulation. Several methods have been proposed to detect and locate the tampered regions, while many methods failed when the copied region undergone some geometric transformations before being pasted, because of the de-synchronization in the searching procedure. This paper presents an efficient technique for detecting the copy-move forgery under geometric transforms. Firstly, the forged image is divided into overlapping circular blocks, and Polar Complex Exponential Transform (PCET) is employed to each block to extract the invariant features, thus, the PCET kernels represent each block. Secondly, the Approximate Nearest Neighbor (ANN) Searching Problem is used for identifying the potential similar blocks by means of locality sensitive hashing (LSH). In order to make the algorithm more robust, morphological operations are applied to remove the wrong similar blocks. Experimental results show that our proposed technique is robust to geometric transformations with low computational complexity.     

同幅数字图像中Copy-Move型篡改的盲检测

为了快速有效地检测复制-粘贴(Copy-Move)图像篡改,提出了一种基于重叠块统计值的Copy-Move型篡改图像盲认证方式.该算法先将图像进行一次离散小波变换(Discrete wavelet transform,DWT)并取其低频部分分解为重叠块,接着统计各重叠块的7个统计值并计算重叠块间的相似性找出相似块,最后返回原篡改图像找出篡改部分.仿真结果表明,该方法能快速有效地检测出篡改部分经过JPEG有损压缩、高斯白噪声污染和这两者结合的篡改图像.     

一种运动自适应的帧速率上转换算法

在基于块的帧速率上转换算法中,块尺寸直接影响运动估计的准确性及插值帧的视觉效果。为此,提出了一种运动自适应的帧速率上转换算法。通过引入STGS图对视频内容进行预分析,根据运动特性自适应选取适合每一帧图像的块尺寸,进行重叠的块运动估计。并针对失真易产生在块边缘的特点,对块边缘的运动矢量进行插值细化处理,平滑运动矢量场,减轻人眼敏感的块效应。实验结果表明,该算法整体性能优于传统的固定块尺寸运动估计的帧速率上转换算法。     

小波低频子带提取特征的图像复制篡改检测

针对数字图像取证中一类常见的图像篡改-复制粘贴图像伪造,提出了一种利用小波变换和图像块灰度分布提取特征的检测算法。相对于原图,小波分解的低频子带仍然保持原图像的概貌和空间特性,但在尺寸上减小了很多;对小波低频子带进行重叠分块,再对各重叠块进行灰度分布特征的提取;利用迭代划分法结合相似性匹配搜索相似图像块,进一步减少了检测的计算量;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。实验表明该算法能够较精确地定位出复制和粘贴的图像伪造区域,并有效地减少了运算量,提高了检测效率。     

基于分块特征收缩的行人检测方法

针对基于梯度方向直方图(Histogram of Oriented Gradient,HOG)特征和局部二值模式(Local Binary Patterns,LBP)特征的行人检测存在特征向量维度大、检测精度有待提高的问题,提出了一种分块特征收缩的行人检测方法。首先将样本图像划分成多个大小相同的重叠分块;然后提取各分块的HOG和LBP特征,并将两种特征融合作为分块的特征,通过该特征来训练分块分类器,根据分块分类器的行人检测精度对分块进行排序,选取检测精度较高的分块进行特征收缩;最后将特征收缩后的分块特征向量连接在一起作为最终用于行人检测的特征。在INRIA公共测试集合上的实验结果表明,该方法在降低了特征向量维度的同时提高了行人检测精度。     

基于分形和统计的复制-粘贴篡改图像的检测

现在大多数图像“复制-粘贴”篡改检测算法对于区域复制后的进一步混合处理不能进行有效检测。为此提出了一种新的基于分形和统计的检测方法。首先将图像分块并提取每块的特征向量,该特征向量由分形维数和三个统计量组成;接着对所有特征向量进行字典排序;最后,利用图像块的位置信息和欧氏距离定位篡改区域。此方法不仅能够检测传统的复制-粘贴型篡改,而且还能够检测经过旋转、翻转以及旋转和翻转混合的多区域复制-粘贴型篡改;此方法也能够抵抗高斯模糊、对比度调整和亮度调整等攻击。实验结果表明了该方法的有效性。     

抗几何攻击的量化鲁棒水印技术研究

提出了一种能够有效抵抗旋转、平移、缩放等基本几何攻击的鲁棒图像水印方案,在其嵌入方案中,提出一种几何不变量——基于图像圆区域内的统计特征不变性。利用该不变性,可以较好地矫正几何形变。在嵌入方案中,实现了在一个嵌入点上嵌入2比特水印信息量,并且利用HVS特性,把水印自适应地嵌入在视觉不敏感区域的纹理方向上。在水印提取方案中,利用特征圆形区域矫正形变,然后在DCT变换域中盲提取水印。实验结果表明,该水印方案具有很好的透明性,同时具有较强的抵抗基本几何形变能力,并且对于一般的图像处理、JPEG压缩等攻击具有较好的鲁棒性。     

A new robust video watermarking algorithm based on SURF features and block classification

In this paper, we propose a robust block classification based semi-blind video watermarking algorithm using visual cryptography and SURF (Speed-Up Robust Features) features to enhance the robustness, stability, imperceptibility and real-time performance. A method of selecting the best frames in each shot and the best regions or blocks within best frames is proposed to avoid employing frame–by-frame method for generating owner’s share in order to enhance robustness as well as reducing time complexity. In our method, Owner’s share is generated using the classification of selected robust blocks within the chosen frames along with corresponding watermark information. In extraction process, the SURF features are employed to match the feature points of selected frames with all frames to detect selected frames. Moreover, we resynchronize the embedded regions from distorted video to original sequence using SURF feature points matching. Afterwards, based on these matched feature points, rotation and scaling parameters are estimated next, selected blocks are retrieved using side information being stored eventually, watermark information is reconstructed successfully. Selecting Best frames, best regions, and employing surf features make our method to be highly robust against various kinds of attacks including image processing attacks, geometrical attacks and temporal attacks. Experimental results confirm the superiority of our scheme in case of being applicable in the real world, enhancing robustness and exploiting idea imperceptibility, over previous related methods.     

采用圆谐-傅里叶矩的图像区域复制粘贴篡改检测

现有检测方法大多对图像区域复制粘贴篡改的后处理操作鲁棒性不高.针对这种篡改技术,提出一种新的基于圆谐-傅里叶矩的区域篡改检测算法.首先将图像分为重叠的小块;然后提取每个图像块的圆谐-傅里叶矩作为特征向量并对其进行排序;最后根据阈值确定相似块,利用位移矢量阈值去除错误相似块以定位篡改区域.实验结果表明,该算法能有效抵抗噪声、高斯模糊、旋转等图像后处理操作,且与基于HU矩的方法相比有更好的检测结果.     

Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and significant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying original photographic images to generate a forged image is known as digital image forging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery. In order to make the forgeries real and inconspicuous, geometric or post-processing techniques are frequently performed on tampered regions during the tampering process. In Copy-Move forgery detection, the high similarity between the tampered regions and the source regions has become crucial evidence. The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform (DCT) components as block representations. Due to the high dimensionality of the feature space, Gaussian Radial basis function (RBF) kernel based Principal component analysis (PCA) is used to minimize the dimensionality of the feature vector representation, which improves feature matching efficiency. In this paper, we propose to use a novel enhanced Scale-invariant feature transform (SIFT) detector method called as RootSIFT, combined with the similarity measures to mark the tampered areas in the image. The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity, detection reliability, and forgery location accuracy, according to the experimental results. The F1 score of the proposed method is 92.3% while the literature methods are around 90% on an average.     

基于TSVD的图像复制区域伪造检测算法

提出了一种有效的盲检测算法来识别图像复制区域伪造。该算法采用截尾奇异值分解(truncatedsingular value decomposition,TSVD)变换来处理图像块数据,并对图像块进行相似性匹配检测。实验结果表明,本算法具有较强的检测能力,能够有效抵抗多种修饰操作,如JPEG有损压缩、高斯模糊、高斯白噪声污染等。     

Forgery detection using feature-clustering in recompressed JPEG images

JPEG images are widely used in a large range of applications. The properties of JPEG compression can be used for detection of forgery in digital images. The forgery in JPEG images requires the image to be resaved thereby, re-compression of image. Therefore, the traces of recompression can be identified in order to detect manipulation. In this paper, a method to detect forgery in JPEG image is presented and an algorithm is designed to classify the image blocks as forged or non-forged based on a particular feature present in multi-compressed JPEG images. The method performs better than the previous methods which use the probability based approach for detecting forgery in JPEG images.