CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

Soni, Badal; Das, Pradip K.; Thounaojam, Dalton Meitei

doi:10.1049/iet-ipr.2017.0441

Cited by 69 publications

(27 citation statements)

References 74 publications

(97 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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“…Forgery is localized by highlighting the similar blocks in an image. Distinctive researchers make utilization of different types of features [14]. These researchers are classified according to the type of feature used in their methods as shown in Figure 8.…”

Section: Copy-move Forgery Detection Techniquesmentioning

confidence: 99%

An Analysis of Image Forgery Detection Techniques

Kaur

Kanwal

2019

Stat., optim. inf. comput.

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Society is becoming increasingly dependent on the internet and so does it become more and more vulnerable to harmful threats. These threats are becoming vigorous and continuously evolving. These threats distorts the authenticity of data transmitted through the internet. As we all completely or partially rely upon this transmitted data, hence, its authenticity needs to be preserved. Images have the potential of conveying much more information as compared to the textual content. We pretty much believe everything that we see. In order to preserve/check the authenticity of images, image forgery detection techniques are expanding its domain. Detection of forgeries in digital images is in great need in order to recover the peoples trust in visual media. This paper is going to discuss different types of image forgery and blind methods for image forgery detection. It provides the comparative tables of various types of techniques to detect image forgery. It also gives an overview of different datasets used in various approaches of forgery detection.

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“…There are five methods in block based approach. They are moment-based; dimensionality reduction-based, frequency-based, intensity-based and textured based [10]. Popescu et al [1], PCA is applied to these blocks to yield a reduced dimensional representation that is robust to minor variance in the images such as lossy compression or additive noise.…”

Section: Block-based Approachmentioning

confidence: 99%

“…L i represents the temporary scale image in the jth cycle of FED matrix. Matrix A used for calculation with respect to the method specified in [10]. Scale images Li created after each n cycle is given below…”

Section: Preprocessingmentioning

confidence: 99%

Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

Soni*,

Reddy.V,

Muppalaneni

et al. 2019

IJITEE

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Image Forgery is an illegal activity in the society as per cyber laws. There are various types of forgeries in which forgery on images is considered as an illegal activity. Image forgery may take place in different ways. One way for doing forgery on images is copy and move forgery which may result in loss of image integrity or authenticity. There are number of popular detection techniques exist such as SIFT, SURF etc., but have high complexity in detection of forgery. Here we have proposed a method to detect the forgery on images which results in loss of integrity or authenticity. In our proposed method we have used descriptor matching using Trie Data Structure The descriptor matching method of implementation using Trie data structure made the complexity of the problem to reduce to O (n log n). Using Key points approach we can verify the integrity of the image. Extracting the features with key points approach is computational expensive task. But there is KAZE method which overcomes this situation. KAZE’s method of using non-linear diffusion filtering requires it to solve a series of PDEs. This cannot be done analytically forcing KAZE to use a numerical method called an AOS scheme to solve the PDEs. However, this process is computationally costly and therefore an accelerated version of KAZE was created. The Accelerated KAZE or AKAZE which creates non-linear scale space through Fast Explicit Diffusion for reduce the complexity in extracting the features.

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CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

Cited by 69 publications

References 74 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

An Analysis of Image Forgery Detection Techniques

Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

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