Copy-move forgery detection based on PHT

Li, Leida; Li, Shushang; Wang, Jun

doi:10.1109/wict.2012.6409232

Cited by 31 publications

(23 citation statements)

References 8 publications

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“…A late study by Li, Li & Wang [41], depicts the blockmatching method of copy-move image forgery detection by utilizing Polar Harmonic Transform (PHT). They utilized this new sort of orthogonal moment in order to make features of image blocks and they achieved bock-matching by utilizing PHT block features.…”

Section: Frequency-based Methodsmentioning

confidence: 99%

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Kaur¹

2016

IJCA

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With the development of Image processing editing tools and software, an image is manipulated very easily. The image manipulation detection is essential for the reason that an image can be employed as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copymove or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, counterfeiters utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. In the literature part, various researchers portrayed the working scenario of copy-move image forgery utilizing the similarity measures as well as the relationship among the original parts of the image and their pasted parts in the similar image. This research paper illustrates recent issues in the techniques of forgery detection and also all their comparative analysis.

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Section: Frequency-based Methodsmentioning

confidence: 99%

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Kaur¹

2016

IJCA

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“…In preprocessing, one seeks to improve the image data by suppressing undesired distortions or enhancing the image features (Miljkovi, 2009). The conversion of RGB (Red, Green, and Blue) color channels to grayscale appears to be the most frequently used method used in pre-processing (see (Vincent Christlein, 2010;Amerini et al, 2011;Ardizzone et al, 2010Ardizzone et al, , 2009Cao et al, 2012;Huang et al, 2008Huang et al, , 2011L. Li et al, 2012;Li and Yu, 2010;Lynch et al, 2013;Muhammad et al, 2012;Myna et al, 2008;Peng et al, 2011;Ryu et al, 2013;Wang et al, 2012;Yang et al, 2013;Yang and Huang, 2009;Zhang et al, 2008;Zhao and Guo, 2013).…”

Section: Workflowmentioning

confidence: 99%

“…On the other hand, the keypoint-based approach is commonly displayed by line transformation between each matching point. Both visualization can be further refined by morphology operation using the shapes properties of the features such as contours, skeletons and convex hulls (see Amerini et al, 2013;Cao et al, 2012;Jaberi et al, 2013aJaberi et al, , 2013bL. Li et al, 2012;Pan and Lyu, 2010;Peng et al, 2011;Yang and Huang, 2009;Zhang et al, 2008;Zhao and Guo, 2013).…”

Section: Workflowmentioning

confidence: 99%

“…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. Li et al, 2014L. Li et al, , 2012Li and Yu, 2010;Li, 2013;Wu et al, 2010) Dimension Reduction (Ting and Rang-Ding, 2009;Yang and Huang, 2009;Zhao, 2010) Others Wang et al, 2012) Frequency Transform.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

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

Warif

Wahab

Idris

et al. 2016

Journal of Network and Computer Applications

180

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“…A method proposed by Ghorbani, Firouzmand and Faraahi [19] for detection of copy-move forgery. They used Discrete Wavelet Transform for dividing input image into four sub-bands.…”

Section: (D) Frequency-based Techniquesmentioning

confidence: 99%

Copy-Move Image Forgery Detection a Review

Dixit¹,

Gupta²

2016

IJIGSP

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Abstract-Due to the availability of various image processing tools forgery over an image can be performed very easily but very difficult to identify. In copy-move forgery, a segment is copied from the original image and pasted at some other location on the same image to hide significant objects of image or to bring additional information which is originally not present in image. Nowadays, this forgery technique is drawing researcher"s attention. Till now many solutions are presented by researchers to detect such type of forgery in images. Several post-processing operations like rotation, alteration in intensity, noise addition, filtering and blurring can be applied over copy-moved segment which makes detection of forgery very difficult. Copy-move forgery detection is mainly based on finding similarity present in an image and establish a relationship between genuine image parts and pasted portion of the image. This paper is centralized towards providing survey to forgery detection techniques based on different block-based methods. In block-based methods image is divided in blocks of fixed dimension and further features are extracted corresponding to each block of image. Forged blocks are identified utilizing the similarity present between feature vectors.

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Copy-move forgery detection based on PHT

Cited by 31 publications

References 8 publications

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

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

Copy-Move Image Forgery Detection a Review

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