Survey of copy-paste forgery detection in digital image forensic

Tembe, Anushree U.; Thombre, Supriya

doi:10.1109/icimia.2017.7975613

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…The main novelty of the work consists in introducing a clustering procedure which operates in the domain of the geometric transformation; such a procedure has been properly improved to deal with multiple cloning too. Anushree U. Tembe1 et al [33]have explained based approach used, in which relevant keypoints are extracted using Scale Invariant Feature Transforms. Interested points are extracted from the image, and extracted points are modelled as a set of connected triangles using Delaunay Triangulation Copy-paste image forgery where one portion of an image is replaced with the same image.…”

Section: Literature Workmentioning

confidence: 99%

A Review on Copy Move Forgery Detection in Document Images

2022

IRJMETS

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With billions of digital images folding the internet which are widely used and regards as the major information source in many fields in recent years with the high advance of technology it may seen to fraud the image. In digital images copy move forgery is the most common image tempering objective of copy move forgery may be to conceal some unwanted features, or to add some local feature which are otherwise absent . In the technological development in digital world has led to a huge increase in the popularity of digital images all domains of life. Thus there need to authenticate images especially in legal matters. Copy move forgery involves copying a portion of an image and pasting it to different location in same image. With a purpose to conceal facts. This paper presents a study of various image forgery techniques and datasets. A Comparative analysis of major techniques is also presented.

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Section: Literature Workmentioning

confidence: 99%

A Review on Copy Move Forgery Detection in Document Images

2022

IRJMETS

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“…where G σ denotes a Gaussian low-pass filter with a variance of σ . The sharpening image I sp (m, n) can be represented by (6),…”

Section: Related Work a Usm Sharpeningmentioning

confidence: 99%

“…When the tampered images are used in newspapers, court evidence and other fields, it may have a negative impact on society. Therefore, digital image forensic technology has become one of the hot research issues in the past decades [1]- [6].…”

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confidence: 99%

Image Sharpening Detection Based on Difference Sets

2020

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Image sharpening is one of the basic operations used to improve the visual effect of images. Image editing makes it impossible to confirm the authenticity of an image, and the purpose of image forensics is to detect whether an image has been artificially edited. To solve this problem, a forensic algorithm based on global image pixel values is proposed. Twelve difference sets composed of first-order and second-order differences in different directions are used as the image feature to reveal the difference between image pixels. Additionally, the feature is used to determine whether the image has been sharpened or not. To verify the performance of the algorithm, a series of experiments are performed on different formats of image datasets. Compared with the existing forensic algorithms based on traditional machine learning, the detection accuracy of the proposed method is greatly improved.

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“…In [8], a survey was presented on various kinds of digital image forgeries detection methods, this survey focusing on the current methods for forged image. In [9], short review of CMFD demonstrate that the work is still state of art and there is plenty of room for future study. In [10], a state-ofthe-art review and analysis of recent CMFD techniques are presented.…”

Section: Introductionmentioning

confidence: 99%

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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Survey of copy-paste forgery detection in digital image forensic

Cited by 11 publications

References 16 publications

A Review on Copy Move Forgery Detection in Document Images

A Review on Copy Move Forgery Detection in Document Images

Image Sharpening Detection Based on Difference Sets

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

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