Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features

Soni, Badal; Das, Pradip K.; Thounaojam, Dalton Meitei

doi:10.1016/j.jisa.2019.01.007

Cited by 30 publications

(11 citation statements)

References 16 publications

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“…Block-based techniques separate the image into overlapping or non-overlapping squares or blocks of circle shapes as shown in Figure 5. Then using an effective feature transform, features can be extracted from each block such as discrete cosine transform [37,38], discrete wavelet transform [39], curvelet transform [40], fourier transform [41,42,43], fast walsh-hadamard transform (fwht), singular value decomposition [44], principal component analysis, intensity, zernike moments [45], and combinations of them. Some of multiscale decomposition transform (MSD) like pyramid and wavelet transform lacks directionality.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

“…d) The CMFD that proposed in [40], are robust to some of geometric transformation. e) In [37], only geometric transformation are applied. f) In [36], only post processing operations are applied.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

“…However, it fail for other operations especially contrast change. b) The CMFD that proposed in [37], [26], [36], [38] are very robust to most of the geometric transformation. However, it fail for other geometric transformation such as translation.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

“…The image may be exposed to both or either of the post-processing operations and geometric transformations, which may disturb the correlation between copy-moved regions then make it more difficult to distinguish duplicated areas. In order to detect multiple forgeries present in the image and to detect precise forgery in extremely same areas, IC-MFDs in [37], [42] needs to some improvement. Nevertheless, selecting the best feature extraction methods and effective matching technique will easily unveil forgery.…”

Section: Comparison Of Ic-mfd Techniquesmentioning

confidence: 99%

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A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Ahmed

Hammad

Jamil

2021

IJEECS

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<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

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Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

Section: Comparison Of Ic-mfd Techniquesmentioning

confidence: 99%

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A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Ahmed

Hammad

Jamil

2021

IJEECS

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show abstract

“…Soni et al [23] proposed a combination of block‐based and keypoint‐based copy–move forgery detections. SURFs were extracted from each non‐overlapping block and 2NN matching is performed against the blocks.…”

Section: Related Workmentioning

confidence: 99%

Enhanced copy–paste forgery detection in digital images using scale‐invariant feature transform

Selvaraj¹,

Muneeswaran²

2020

IET Image Processing

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A probabilistic framework for copy-move forgery detection based on Markov Random Field

Elhaminia

Harati

Taherinia

2019

Multimed Tools Appl

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Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features

Cited by 30 publications

References 16 publications

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Enhanced copy–paste forgery detection in digital images using scale‐invariant feature transform

A probabilistic framework for copy-move forgery detection based on Markov Random Field

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