An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

Hegazi, Aya; Taha, Ahmed; Selim, Mazen M.

doi:10.1016/j.jksuci.2019.07.007

Cited by 28 publications

(24 citation statements)

References 23 publications

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“…The optimal CMFD can be measured in terms of the robust to all post processing operations, all geometric transformation, detect multiple forgeries, And High accuracy of detection. From Table 2, it can be see the following observations: a) The CMFD that proposed in [28], [29], [30], [31], are very robust to most of post-processing operations.…”

Section: Lists the Previous Recently Ic-mfd Algorithms According To Feature Extracted From Spatial Domainmentioning

confidence: 92%

“…The detection performance of the 15 IC-MFDs methods in multiple domains in term of true & false positive metrics are compared. It can be observed that IC-MFDs based on spatial domain [31] tends to low false positive rate than the rest methods. However, [29] tends to high false positive rate than the rest methods.…”

Section: Comparison Of Ic-mfd Techniquesmentioning

confidence: 94%

“…However, it fail for other operations especially contrast change. b) The CMFD that proposed in [32], [29], [30], [33], [31] are very robust to most of the geometric transformation. However, it fail for other geometric transformation such as translation.…”

Section: Lists the Previous Recently Ic-mfd Algorithms According To Feature Extracted From Spatial Domainmentioning

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: Lists the Previous Recently Ic-mfd Algorithms According To Feature Extracted From Spatial Domainmentioning

confidence: 92%

Section: Comparison Of Ic-mfd Techniquesmentioning

confidence: 94%

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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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“…Vaishnavi and Subashini [10] proposed a method for copy move forgery detection by means of "symmetry based local features". Hegazi et al [11] employed "density-based clustering" for the detection of forged images. Lee et al [12] employed "histogram of oriented gradients" for the detection of forged images.…”

Section: Related Workmentioning

confidence: 99%

“…In literature, different types of the DFTs have also been developed to detect the authenticity of the images like "forensics-focused operating systems" (FFOS), (ii) computer forensics, (iii) "memory forensics", (iv) "mobile device forensics", and (v) "software forensics". Based on our review, we identify that there is no classification and evaluation of digital forensic tools (DFTs) in the literature of image forensic science [10,11].…”

Section: Introductionmentioning

confidence: 99%

Classification and evaluation of digital forensic tools

2020

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Digital forensic tools (DFTs) are used to detect the authenticity of digital images. Different DFTs have been developed to detect the forgery like (i) forensic focused operating system, (ii) computer forensics, (iii) memory forensics, (iv) mobile device forensics, and (v) software forensics tools (SFTs). These tools are dedicated to detect the forged images depending on the type of the applications. Based on our review, we found that in literature of the DFTs less attention is given to the evaluation and analysis of the forensic tools. Among various DFTs, we choose SFTs because it is concerned with the detection of the forged digital images. Therefore, the purpose of this study is to classify the different DFTs and evaluate the software forensic tools (SFTs) based on the different features which are present in the SFTs. In our work, we evaluate the following five SFTs, i.e., "FotoForensics", "JPEGsnoop", "Ghiro", "Forensically", and "Izitru", based on different features so that new research directions can be identified for the development of the SFTs.

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Copy Move Forgery Detection by Using Integration of SLIC and SIFT

Rathi

Singh

2021

Expert Clouds and Applications

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An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

Cited by 28 publications

References 23 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

Classification and evaluation of digital forensic tools

Copy Move Forgery Detection by Using Integration of SLIC and SIFT

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