A Review of Passive Image Cloning Detection Approaches

Doegar, Amit; Dutta, Maitreyee; Kumar, Gaurav

doi:10.1007/978-981-13-1217-5_46

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…To evaluate the accuracy of the proposed algorithm, it has been compared with three algorithms. Figure (6) illustrates the overall accuracy of these forgery detection algorithms.…”

Section: The Experimental Resultsmentioning

confidence: 99%

“…Generally, the main steps in detection of copymove forgery are pre-processing, features extraction, matching, and post-processing. Figure 1 represents a general framework for detection of copy-move forgery [6]. Pre-Processing: Before features extraction, some operations are performed on the images in order to enhance the structural changes that have occurred in the images because of the forgery.…”

Section: Introductionmentioning

confidence: 99%

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False Matches Removing in Copy-Move Forgery Detection Algorithms

Mahdi¹,

Alsaad

2020

MJS

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Today the technology age is characterized by spreading of digital images. The most common form of transfer the information in magazines, newspapers, scientific journals and all types of social media. This huge use of images technology has been accompanied by an evolution in editing tools of image processing which make modifying and editing an image is very simple. Nowadays, the circulation of such forgery images, which distort the truth, has become common, intentionally or unintentionally. Nowadays many methods of copy-move forgery detection which is one of the most important and popular methods of image forgery are available. Most of these methods suffer from the problem of producing false matches as false positives in flat regions. This paper presents an algorithm of the Copy-Move forgery detection using the SIFT algorithm with an effective method to remove the false positives by rejecting all key-points in matches list that own a neighbor less than the threshold. The accuracy of the proposed algorithm was 95 %. The experimental results refer that the proposed method of false positives removing can remove false matches accurately and quickly.

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“…To evaluate the accuracy of the proposed algorithm, it has been compared with three algorithms. Figure (6) illustrates the overall accuracy of these forgery detection algorithms.…”

Section: The Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

False Matches Removing in Copy-Move Forgery Detection Algorithms

Mahdi¹,

Alsaad

2020

MJS

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“…In active approaches images need to be protected through digital signature or through watermarking techniques whereas passive approaches do not require any kind of pre-embed operation of digital signature or watermarking. The drawback of active approaches is that it needs to pre pre-embedded either with digital signature or with watermarking, whereas a large number of images present today on web, social media and other applications are not active in nature [1]. Thus we have focused on the detection of forgery with a passive approach which is described further in given sections.…”

Section: Figure 1 Approaches For Image Forgery Detectionmentioning

confidence: 99%

“…Digital images are being used in various spheres of real-time applications like media, military, science, law, education, politics, medical imaging and diagnosis, art piece, digital forensics, intelligence, sports, photography, social media, scientific publications, journalism, and business [1]. Digital images become a significant resource of information in the digital world as they are the fastest means of information and medium of communication.…”

Section: Introductionmentioning

confidence: 99%

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https://jusst.org/developing-the-rural-economy-structure-in-agriculture-towards-the-betterment-of-indias-economy/

Doegar¹,

Dutta²,

Kumar³

2020

JUSST

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In the present scenario, one of the threats of trust on images for digital and online applications as well as on social media. Individual’s reputation can be turnish using misinformation or manipulation in the digital images. Image forgery detection is an approach for detection and localization of forged components in the image which is manipulated. For effective image forgery detection, an adequate number of features are required which can be accomplished by a deep learning model, which does not require manual feature engineering or handcraft feature approaches. In this paper we have implemented GoogleNet deep learning model to extract the image features and employ Random Forest machine learning algorithm to detect whether the image is forged or not. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the dataset into training and testing dataset and also compared with the state-of-the-art approaches.

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