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Copy- Move Attack Forgery Detection by Using SIFT  [PDF]
Mr. Swapnil H. Kudke,,Dr. Avinash D. Gawande
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: Due to rapid advances and availabilities of powerful image processing software’s, it is easy to manipulate and modify digital images. So it is very difficult for a viewer to judge the authenticity of a given image. Nowadays, it is possible to add or remove important features from an image without leaving any obvious traces of tampering. As digital cameras and video cameras replace their analog counterparts, the need for authenticating digital images, validating their content and detecting forgeries will only increase. For digital photographs to be used as evidence in law issues or to be circulated in mass media, it is necessary to check the authenticity of the image. So In this paper, describes an Image forgery detection method based on SIFT. In particular, we focus on detection of a special type of digital forgery – the copy-move attack, in a copy-move image forgery method; a part of an image is copied and then pasted on a different location within the same image. In this approach an improved algorithm based on scale invariant features transform (SIFT) is used to detect such cloning forgery, In this technique Transform is applied to the input image to yield a reduced dimensional representation, After that Apply key point detection and feature descriptor along with a matching over all the key points. Such a method allows us to both understand if a copy–move attack has occurred and, also furthermore gives output by applying clustering over matched points.
An Efficient Method for Detection of Copy-Move Forgery Using Discrete Wavelet Transform
Er. Saiqa Khan,Er. Arun Kulkarni
International Journal on Computer Science and Engineering , 2010,
Abstract: Copy-Move forgery is a specific type of image forgery, in which a part of digital image is copied and pasted to another part in the same image. This paper describes blind forensics approach for detecting Copy-Move forgery. Our technique works by first applying DWT (Discrete Wavelet Transform) to the input image to yield a reduced dimension representation [1]. Then the compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified using Phase Correlation as similarity criterion. Due to DWT usage, detection is first carried out on lowest level image representation. This approach drastically reduces the time needed for the detection process.
Adaptive Threshold-based Detection Algorithm for Image Copy-move Forgery

KANG Xiao-bing,WEI Sheng-min,

计算机科学 , 2011,
Abstract: Image forgery detection is a burgeoning research field in digital forensics. As a most common way of image tampering, copy-move forgery is used to conceal objects or clone regions to produce a non-existing image scene. The target of copy-move forgery detection is to identify bigger duplicated image regions which are same or extremely similar. We reviewed several methods proposed to achieve this goal. These methods failed in detecting digital images with homogeneous texture or uniform areas and selecting the appropriate thresholds. We presented a novel adaptive thresholdbased detection algorithm for image copy-move forgery,which might be applied to the color images with homogeneous or smooth regions and identified and located forged image regions automatically if only reasonable thresholds were estimated. Experimental results on several forged images with various homogeneous or uniform regions were presented to demonstrate the effectiveness of the proposed algorithm.
Copy-Move Forgery Detection in Digital Images: Progress and Challenges
Sunil Kumar,P. K. Das,Shally,S. Mukherjee
International Journal on Computer Science and Engineering , 2011,
Abstract: With the advancement of technology and easy availability of imaging tools, it’s not difficult now a days to manipulate digital images to hide or create misleading images. Image forgery detection is currently one of the hot research fields of image processing. Many research papers have been published during recent years. Image forgery has already been categorized. Copy-Move forgery is one of thefrequently used techniques. In this paper a review of the existing techniques has been done. An attempt has been made in this paper to list and highlight almost all the proposed methods along with their key features.
Detection of Copy-Move Forgery of Images Using Discrete Wavelet Transform  [PDF]
Preeti Yadav,Yogesh Rathore
International Journal on Computer Science and Engineering , 2012,
Abstract: Digital images are used everywhere and it is easy to manipulate and edit because of availability of various image processing and editing software. In a copy-move image forgery, a part of an image is copiedand then pasted on a different location within the same image. A copy-move image forgery is done either for hiding some image object, or adding more details resulting in at least some part being cloned. In both the case, image reliability is lost. In this paper an improved algorithm based on Discrete Wavelet Transform (DWT) is used to detect such cloning forgery. In this technique at first DWT (Discrete Wavelet Transform) is applied to the input image for a reduced dimensional representation. Then the compressed image isdivided into overlapping blocks. After that Lexicographic sorting is performed, and duplicated blocks are identified. Due to DWT usage, detection is first carried out on lowest level image representation. This approach increases accuracy of detection process and reduces the time needed for the detection process.
Detection of copy-move forgery in digital images based on DCT  [PDF]
Nathalie Diane Wandji,Sun Xingming,Moise Fah Kue
Computer Science , 2013,
Abstract: With rapid advances in digital information processing systems, and more specifically in digital image processing software, there is a widespread development of advanced tools and techniques for digital image forgery. One of the techniques most commonly used is the Copy-move forgery which proceeds by copying a part of an image and pasting it into the same image, in order to maliciously hide an object or a region. In this paper, we propose a method to detect this specific kind of counterfeit. Firstly, the color image is converted from RGB color space to YCbCr color space and then the R, G, B and Y-component are splitted into fixed-size overlapping blocks and, features are extracted from the R, G and B-components image blocks on one hand and on the other, from the DCT representation of the R, G, B and Ycomponent image block. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. Experimental results showed that the proposed method can detect the duplicated regions when there is more than one copy move forged area in the image and even in case of slight rotations, JPEG compression, shift, scale, blur and noise addition.
An Evaluation of Popular Copy-Move Forgery Detection Approaches  [PDF]
Vincent Christlein,Christian Riess,Johannes Jordan,Corinna Riess,Elli Angelopoulou
Computer Science , 2012, DOI: 10.1109/TIFS.2012.2218597
Abstract: A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.
A Comparative Analysis on Copy Move Forgery Detection in Spatial Domain Method Using Lexicographic and Non Lexicographic Techniques  [cached]
Vidya S. Pujari Prof. Mandar Sohani
International Journal of Electronics Communication and Computer Engineering , 2012,
Abstract: A digitally altered image, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic image[1]. Authenticity of digital images plays important role in various fields like medical, legal, criminal, and journalism. Due to rapid advances and availabilities of powerful image processing software, digital images are easy to manipulate and modify for ordinary people. This makes it more and more difficult for a viewer to check the authenticity of a given digital image. In the fields such as forensics, medical imaging, e-commerce, and industrial photography, authenticity and integrity of digital images is essential. This motivates the need for detection tools that are transparent to tampering and can tell whether an image has been tampered just by inspecting the tampered image[3]. Copying parts of an image and pasting in the same image for covering unwanted information or creating a fake image by splicing two or more images are most used techniques in digital image manipulation. These are called copy-move and image-splicing techniques respectively. In this paper we focus on copy-cover image forgery using spatial domain method and do the comparative analysis for lexicographic and non lexicographic techniques.
Copy-Move Forgery Detection Using Slicing Transform

谢伟, 万晓霞, 严文婧, 叶松涛
, WAN Xiaoxia, YAN Wenjing, YE Songtao

- , 2017, DOI: 10.13203/j.whugis20150451
Abstract: 针对图像区域复制(copy-move,C-M)篡改检测方法通常面临的特征向量维度高、计算量大等问题,提出了一种基于分片变换(slicing transform,SLT)的图像C-M篡改检测方法。通过对目标图像进行SLT变换并对图像分片进行分组合并,提取每组图像分片的局部分片密度特征对图像块进行C-M篡改检测。实验结果表明,该方法提出的局部分片密度特征向量能够较好地表征图像特征,较典型的基于分块的篡改检测方法特征维度低,具有较低的时间复杂度和较高的检测率,并且对图像篡改区域的旋转攻击和缩放攻击亦具有较好的鲁棒性
Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction  [PDF]
Soad Samir, Eid Emary, Khaled Elsayed, Hoda Onsi
Journal of Computer and Communications (JCC) , 2019, DOI: 10.4236/jcc.2019.79001
Abstract: Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.
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