Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm

Huang, Hailing; Guo, Wenbin; Yu, Zhengtao

doi:10.1109/paciia.2008.240

Cited by 347 publications

(177 citation statements)

References 3 publications

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“…An example of this approach is demonstrated in the paper [6]. In most of the studies [7][8] kd-tree [9] is used for the nearest feature vectors search. It is used in conjunction with the k nearest neighbors search algorithm, which allows to generate a list of k nearest vectors to every feature vector in Euclidean space.…”

Section: Fig 1 General Scheme Of a Copy-move Detection Algorithmmentioning

confidence: 99%

Copy-Move Detection Algorithm Efficiency Increase Using Binary Space Partitioning Trees

Kuznetsov¹,

Myasnikov²

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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Abstract.Duplicates embedding is one of the most frequently used type of digital image change. The copy-move procedure includes copying an image fragment from one part and pasting it to another part of the same image. Moreover, this fragment can be changed using some transformations, like affine or contrast enhancement. Existing copy-move detection methods consist of two main steps: feature calculation in overlapping processing windows and the nearest feature search in Euclidean space using lexicographic sorting or kd-tree search. In this paper we suggest to use binary space partitioning trees on the second step of the detection algorithm -vp-tree (vantage-point tree). We present the search speed comparison using vp-tree and kd-tree. The results show advantage of the proposed solution over kd-tree use.Keywords: duplicate, copy-move, forgery, kd-tree, vp-tree, binary space partitioning tree Citation: Kuznetsov A, Myasnikov E. Copy-move detection algorithm efficiency increase using binary space partitioning trees.

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Section: Fig 1 General Scheme Of a Copy-move Detection Algorithmmentioning

confidence: 99%

Copy-Move Detection Algorithm Efficiency Increase Using Binary Space Partitioning Trees

Kuznetsov¹,

Myasnikov²

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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“…4 Examples of copy-move attacks for object removal (above) and duplication (below). Images were taken from [45] should be noticed, however, that this operation is not always necessary, as the simple processing of an image can convey relevant alteration of both semantics and pragmatics of an image, as in the well-known case of the TIME magazine cover depicting a darkened O. J. Simpson portrait (Fig. 5).…”

Section: A Short Summary Of the Most Common Tampering Techniquesmentioning

confidence: 99%

“…In this regard, the work of Huang et al [45] exploits SIFT features to obtain robust tampering detection even under geometric transforms. In this case, only matching SIFT key-points are retrieved, by means of the best-bin-first nearest neighbor identification.…”

Section: Detecting Tampering Performed On a Single Imagementioning

confidence: 99%

Digital image forensics: a booklet for beginners

Redi

Taktak

Dugelay

2010

Multimed Tools Appl

307

115

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Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

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“…Keypointbased methods, however, can relatively reliably compensate these shortcomings. In (Amerini et al 2013;Amerini et al 2011;Huang et al 2008) Scale-invariant feature transform (SIFT) and (Bo et al 2010;Jing and Shao 2012) Speed-up robust features (SURF) were used to detect copy-move forgery. Due to the number of keypoints is much less than the number of blocks, computational complexity is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Liu

Ren

et al. 2016

Multidim Syst Sign Process

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Abstract. 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 nonsmooth (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.

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Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm

Cited by 347 publications

References 3 publications

Copy-Move Detection Algorithm Efficiency Increase Using Binary Space Partitioning Trees

Copy-Move Detection Algorithm Efficiency Increase Using Binary Space Partitioning Trees

Digital image forensics: a booklet for beginners

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

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