Copy-Rotation-Move Forgery Detection Using the MROGH Descriptor

Yu, Liyang; Han, Qi; Niu, Xiamu

doi:10.1109/ic2e.2014.54

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Performance analysis of proposed methods [24], [34], [35], [36], [38], [39], [40], [42], [43], [44], [45], [46], [47], [48], [50], [52], [53], [57] and [58] is shown in Figure 5, which have detection accuracy 99.5%, 91%, 91%, 99%, 99%, 94%, 95.2%, 96.23%, 95%, 86.7%, 95%, 99.6%, 77%, 93%, 99.3%, 99.9%, 92.6%, 99.62%, and 100% respectively. Figure 6 shows performance analysis of proposed methods [25], [29], [32], [33], [44] and [56], which have efficiency 98%, 95%, 99%, 99.52%, 95.60% and 96.50% respectively.…”

Section: Comparative Results and Discussionmentioning

confidence: 99%

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An Evaluation of Digital Image Forgery Detection Approaches

Kashyap¹,

Parmar²,

Agrawal³

et al. 2017

Preprint

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With the headway of the advanced image handling software and altering tools, a computerized picture can be effectively controlled. The identification of image manipulation is vital in light of the fact that an image can be utilized as legitimate confirmation, in crime scene investigation, and in numerous different fields. The image forgery detection techniques intend to confirm the credibility of computerized pictures with no prior information about the original image. There are numerous routes for altering a picture, for example, resampling, splicing, and copymove. In this paper, we have examined different type of image forgery and their detection techniques; mainly we focused on pixel based image forgery detection techniques.

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Section: Comparative Results and Discussionmentioning

confidence: 99%

“…L. Yu [53] proposed a method to detect copy-rotation-move forgery detection using the MROGH descriptor. This paper Fig.…”

Section: Pixel Based Existing Image Forgery Detection Techniquesmentioning

confidence: 99%

An Evaluation of Digital Image Forgery Detection Approaches

Kashyap¹,

Parmar²,

Agrawal³

et al. 2017

Preprint

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show abstract

“…In a similar sense, a few picture scientific strategies have been created that especially show relics introduced by various periods of the imaging strategy. Geometry-based picture forgery recognition techniques are isolated into two classes, for example, rule point and metric estimation Figure 1 shows the basic framework for forgery detection [2]. Fridrich et al [20] proposed a technique for distinguishing copymove picture forgery in 2003.…”

Section: Geometry-based Picture Forgery Locationmentioning

confidence: 99%

Image Tampering Detection Techniques: A Statistical Review

Patil¹,

Jariwala²

2019

helix

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Copyrights and digital signatures have boosted a wide variety of research areas in image security domain. Even though these systems are very secure, but there exists a gap between copyrighting an image, and an attacker actually using the image for their own purposes. Usually attackers will try to remove or alter with the original signatures present in the image, and thereby there is a need of forgery detection algorithms in order to track the validity of the image under test. In this paper we provide a statistical review of various algorithms used to detect forgery of images, and focus on algorithms which have good accuracy of forgery detection upon comparison

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