Image Copy Move Forgery Detection: A Review

Mushtaq, Saba; Mir, Ajaz Hussain

doi:10.14257/ijfgcn.2018.11.2.02

Cited by 16 publications

(8 citation statements)

References 34 publications

(58 reference statements)

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“…By analyzing the results in Table 1 , it can be seen that the image segmentation accuracy of the Literature [ 20 ] algorithm is the lowest, Not more than 0.50, followed by the Literature [ 16 ] and Literature [ 19 ] algorithms, and the image segmentation accuracy is below 0.60. Literature [ 15 ] and Literature [ 17 ] have relatively high accuracy rates, especially Literature [ 6 ] can reach up to 0.76, while the segmentation accuracy of this method is between 0.95–0.99. It can be seen that under different data size conditions, the segmentation accuracy of other Literature algorithms is far below the method in this paper.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Computed tomography sequence image has the advantages of real-time, non-destructive, low cost, etc., and has been widely used in disease prevention, diagnosis and treatment. Computed tomography image processing methods have become one of the main directions of blood lipid at home and abroad [ 6 , 7 ]. Deep learning is widely used in medical image processing, represented by convolutional neural networks, it has developed rapidly in recent years [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Optimization algorithm of CT image edge segmentation using improved convolution neural network

Wang

Wei

2022

PLoS ONE

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To address the problem of high failure rate and low accuracy in computed tomography (CT) image edge segmentation, we proposed a CT sequence image edge segmentation optimization algorithm using improved convolution neural network. Firstly, the pattern clustering algorithm is applied to cluster the pixels with relationship in the CT sequence image space to extract the edge information of the real CT image; secondly, Euclidean distance is used to calculate similarity and measure similarity, according to the measurement results, convolution neural network (CNN) hierarchical optimization is carried out to improve the convergence ability of CNN; finally, the pixel classification of CT sequence images is carried out, and the edge segmentation of CT sequence images is optimized according to the classification results. The results show that the overall recognition rate of this method is at a high level. The training time is obviously reduced when the training times exceed 12 times, the recall rate is always about 90%, and the accuracy of image segmentation is high, which solves the problem of large failure rate and low accuracy.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Wang

Wei

2022

PLoS ONE

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“…The job of detecting picture fraud among legitimate and counterfeit photos is a binary classification example [79][80][81][82][83]. This part will go through the specifics of detecting forgeries using conventional matching and deep learning approaches.…”

Section: Review Of Classification and Deep Learning Methodsmentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

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“…Saba Mush taq et al [7] have Explained First of all is the issue of a common benchmark. The challenge of lack of a common benchmark limits the comparability and reproducibility of presently available algorithms.…”

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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Image Copy Move Forgery Detection: A Review

Abstract: Abstract

Cited by 16 publications

References 34 publications

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Optimization algorithm of CT image edge segmentation using improved convolution neural network

A Thorough Investigation on Image Forgery Detection

A Review on Copy Move Forgery Detection in Document Images

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