A fast DCT based method for copy move forgery detection

Kumar, Sunil; Desai, J. V.; Mukherjee, S.

doi:10.1109/iciip.2013.6707675

Cited by 32 publications

(14 citation statements)

References 7 publications

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“…The major difference among them is related to their block representation methods. PCA (Principal Component Analysis), DCT and SVD (Singular Value Decomposition) are some features which are also employed [14][15][16][17][18][19][20]. It is shown that these features are mostly robust against JPEG compression, additive noise and blurring [21,22].…”

Section: Related Workmentioning

confidence: 99%

Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Zandi

Mahmoudi-Aznaveh

Talebpour

2016

IEEE Trans.Inform.Forensic Secur.

132

113

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In this paper, we propose a novel copy-move forgery detection scheme which can accurately localize duplicated regions with a reasonable computational cost. To this end, a new interest point detector is proposed utilizing the advantages of both block-based and traditional keypoint-based methods. The detected keypoints adaptively cover the entire image, even low contrast regions, based on a uniqueness metric. Moreover, a new filtering algorithm is employed which can effectively prune the falsely matched regions. Considering the new interest point detector, an iterative improvement strategy is proposed. The whole procedure is iterated along with adjusting the keypoints density based on the achieved information. The experimental results demonstrate that the proposed scheme outperforms the state-of-the-art methods using two public benchmark databases.

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Section: Related Workmentioning

confidence: 99%

Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Zandi

Mahmoudi-Aznaveh

Talebpour

2016

IEEE Trans.Inform.Forensic Secur.

132

113

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“…Author(s) Frequency Transform (Cao et al, 2012;Deng et al, 2012;Huang et al, 2011;Ketenci and Ulutas, 2013;Kumar et al, 2013;Li et al, 2008;X. Li et al, 2012;Muhammad et al, 2012;Murali et al, 2012;Myna et al, 2008;Peng et al, 2011;Shao et al, 2012;Shin, 2013;Yang et al, 2013;Zhang et al, 2008;Zhao and Guo, 2013) Texture & Intensity (Ardizzone et al, 2009;Bravo-Solorio and Nandi, 2011;Davarzani et al, 2013;Gan and Zhong, 2014;Hsu and Wang, 2012;Kuznetsov Andrey Vladimirovich, 2014;Lin et al, 2009;Lynch et al, 2013;Singh and Raman, 2012;Ulutas and Ulutas, 2013;Ulutaş et al, 2013) Moments Invariant (Bilgehan and Uluta, 2013;Kashyap and Joshi, 2013;Le and Xu, 2013;Mahdian and Saic, 2007;Ryu et al, 2013Ryu et al, , 2010 Log Polar Transform (Bayram et al, 2009;L.…”

Section: Feature Extraction Techniquesmentioning

confidence: 97%

“…Meanwhile, Figure 6(c) represents the breakdown for each type of matching techniques published between 2007 and 2014. (Ardizzone et al, 2009;Bilgehan and Uluta, 2013;Bravo-Solorio and Nandi, 2011;Cao et al, 2012;Davarzani et al, 2013;Gan and Zhong, 2014;Huang et al, 2011;Ketenci and Ulutas, 2013;Kumar et al, 2013;Le and Xu, 2013;L. Li et al, 2012L.…”

Section: Block-based Matching Techniquesmentioning

confidence: 99%

Copy-move forgery detection: Survey, challenges and future directions

Warif

Wahab

Idris

et al. 2016

Journal of Network and Computer Applications

170

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“…Image retouching is not much risky type connected with the different digitally forged image compared to the other methods. Figure 6 [9] shows the image retouching that consist of two images i.e. original image and image retouching.…”

Section: Fig5 Image Splicingmentioning

confidence: 99%

Image Forgery Detection Using Dct And Quantization Matrix Techniques

Abraham*,

Rodrigues,

Fernandes

2019

IJEAT

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In today’s era the image has become useful for communication purpose. But due to the development of software and various techniques it is possible to change images in adding or removing essential feature from it without leaving a clue of real image. It is not easy for the common people to identify whether the image original or tampered. In order to avoid this problem, forgery detection came into existence. Detection of forgery refers to task of image processing to identify that the images are unique or tampered. Several techniques have been used in order to detect the forgeries from the forged image, but this issue has not yet solved. In order to solve these issues we have used Discrete Cosine Transformation (DCT) and quantization matrix techniques for identifying forged areas of image, where the quality of image is not reduced. The Discrete Cosine Transformation (DCT) is used in order for characterizing the overlapping blocks and quantization matrix is used to compress DCT values and gives both highly compressed and best decompressed image quality. Here we use block matching algorithm. This algorithm one of the most frequently used for detecting image which is duplicate. This proposed work also supports for different kinds of images such as JPEG, JPG or PNG of any size it can be either mxn or nxn.

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A fast DCT based method for copy move forgery detection

Cited by 32 publications

References 7 publications

Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Copy-move forgery detection: Survey, challenges and future directions

Image Forgery Detection Using Dct And Quantization Matrix Techniques

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