Detecting copy-move forgery under affine transforms for image forensics

Li, Leida; Li, Shushang; Wu, Xiaoyue

doi:10.1016/j.compeleceng.2013.11.034

Cited by 77 publications

(37 citation statements)

References 12 publications

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“…For a 512 × 512 gray image, Table 1 shows the efficacy of the proposed technique against other existing ones. Compared with [2,8,9,13,17,20], the dimension of the feature vector and the number of blocks of ours are lower, which implies that our proposed technique has a lower computational complexity. The experimental results show that the ability of the proposed technique to detect copy-move forgery in an image is more robust to JPEG compression, noise distortion, rotation, and different scaling attacks.…”

Section: Post-verification and Post-processingmentioning

confidence: 94%

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

Emam

Han

Niu

2015

Multimed Tools Appl

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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.

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Section: Post-verification and Post-processingmentioning

confidence: 94%

“…Unlike most of the existing techniques that use square blocks, circular blocks are adopted here [6,13,14]. The filtered gray scale image is then divided into overlapping circular blocks.…”

Section: Block Tillingmentioning

confidence: 99%

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

Emam

Han

Niu

2015

Multimed Tools Appl

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“…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.…”

Section: Feature Extraction Techniquesmentioning

confidence: 96%

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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“…Leida Li et. al., [15] this paper presents a method for detecting image forgery based on circular pattern matching. The tampered image is filtered and divided into circular blocks.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Work on Image Forgery Detection Technique

Mishra¹,

Rai²

2017

IJCA

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Digital imaging has grown to become the prevalant technology for creating, processing, and storing digital memory and proof. Though this technology brings many leverage, it can be used as a ambiguous tool for covering details and evidences. This is because today digital images can be tampered in such supermacy that forgery cannot be find visually. In fact, the immunity concern of digital content has arisen a long time ago and different methods to verify the efficiency of digital images have been developed. Digital images offer many features for forgery detection algorithm to take precedence of specifically the colour and brightness of individual pixels as well as an image's resolution and format. These properties grant for analysis and similarity between the significance of digital forgeries in an attempt to develop an algorithm for detecting image tampering. This paper presents a technique for copy move image forgery detection using 2-Directional 2-Dimensional Principal Component Analysis (2D)²PCA.

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Detecting copy-move forgery under affine transforms for image forensics

Cited by 77 publications

References 12 publications

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

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

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

A Proposed Work on Image Forgery Detection Technique

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