Copy-move forgery detection: Survey, challenges and future directions

Warif, Nor Bakiah Abd; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Ramli, Rahizar; Salleh, Rosli; Shamshirband, Shahaboddin; Choo, Kim‐Kwang Raymond

doi:10.1016/j.jnca.2016.09.008

Cited by 180 publications

(68 citation statements)

References 106 publications

(225 reference statements)

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“…Keypoint approaches are known to be generally fast, but they can fail if source S and destination D show as homogeneous. Meanwhile, the third category deals with strategies related to irregular region-based methods [29,38], and has been shown to be somewhat efficient, though occasionally resulting in false positives [39][40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

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The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

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Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

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“…The result can be an exact match or approximate one. The features extracted for blocks are in the form of frequency, texture, polar transforms and dimension reduction [26]. Feature extraction techniques are given in Table 1 …”

Section: Block Based Approachesmentioning

confidence: 99%

“…The descriptor helps to increase the reliability of the features to the affine transformation. Then, both features and descriptors in the image are classified and matched to each other to find the duplicated regions in the copy-move forgery [26].…”

Section: Key-point Based Approachesmentioning

confidence: 99%

Image Copy Move Forgery Detection: A Review

Mushtaq¹,

Mir²

2018

IJFGCN

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“…For this purpose, [4] work on images from videos and analyze them with forensics techniques to detect modifications in the images. The problem of image authenticity has been addressed in the Image forensics community in several ways [5], [6]. However most of the approaches poorly perform on images circulating on SN [7].…”

Section: State Of the Artmentioning

confidence: 99%

Fusion-Based Multimodal Detection of Hoaxes in Social Networks

Maigrot

Claveau

Kijak

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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Social networks make it possible to share information rapidly and massively. Yet, one of their major drawback comes from the absence of verification of the piece of information, especially with viral messages. This is the issue addressed by the participants to the Verification Multimedia Use task of Mediaeval 2016. They used several approaches and clues from different modalities (text, image, social information). In this paper, we explore the interest of combining and merging these approaches in order to evaluate the predictive power of each modality and to make the most of their potential complementarity.

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Copy-move forgery detection: Survey, challenges and future directions

Cited by 180 publications

References 106 publications

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Image Copy Move Forgery Detection: A Review

Fusion-Based Multimodal Detection of Hoaxes in Social Networks

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