A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

2019 ◽  
Author(s):  
Gulnawaz Gani ◽  
Fasel Qadir
Keyword(s):  
Cellular Automata ◽  
Digital Image ◽  
Local Binary Patterns ◽  
Forgery Detection ◽  
Novel Method ◽  
Copy Move Forgery Detection ◽  
Detection And Localization

scholarly journals Combining cellular automata and local binary patterns for copy-move forgery detection

2015 ◽  
Vol 75 (24) ◽  
pp. 16881-16903 ◽  
Author(s):  
Dijana Tralic ◽  
Sonja Grgic ◽  
Xianfang Sun ◽  
Paul L. Rosin
Keyword(s):  
Cellular Automata ◽  
Local Binary Patterns ◽  
Forgery Detection ◽  
Copy Move Forgery Detection

Copy-move forgery detection using multiresolution local binary patterns

2013 ◽  
Vol 231 (1-3) ◽  
pp. 61-72 ◽  
Author(s):  
Reza Davarzani ◽  
Khashayar Yaghmaie ◽  
Saeed Mozaffari ◽  
Meysam Tapak
Keyword(s):  
Local Binary Patterns ◽  
Forgery Detection ◽  
Copy Move Forgery Detection

scholarly journals Analysis of Copy Move Forgery Detection Process Using Fuzzy C Means Based DeepLearning Algorithm in Digital Image

2021 ◽  
Author(s):  
Parameswaran Nampoothiri ◽  
Sugitha N
Keyword(s):  
Digital Image ◽  
Test Results ◽  
Forgery Detection ◽  
Fuzzy C Means ◽  
Digital World ◽  
Technological Advances ◽  
Fuzzy C Means Clustering ◽  
Photo Editing ◽  
Copy Move Forgery Detection ◽  
Further Development

Abstract Technological advances in the digital world have led to a tremendous growth in the popularity of digital photography in all walks of life. However, photo editing software tools are easy to use and make photo manipulation a breeze. Therefore, there is a need to find the wrong part of the image. Therefore, this work focuses on finding false images used using the copying process, better known as Copy Move Forgery Detection (CMFD). A copy of Motof spoofing basically means to hide or duplicate a place in a region by attaching certain parts of the same image to it. Initially, digital input images are pre-processed with a Gaussian filter, which is used to blur the image and reduce noise. After further development, a collection of Multi-kernel Fuzzy C-means clustering (MKFCM) was developed to classify images into multiple groups and depending on the various features, the features were extracted using the SIFT algorithm. Finally, with the help of an in-depth reading method, part of the illegal images are found. Test results show that this method is effective and efficient in detecting digital image deception and its functionality and the proposed method is shown in false images.

scholarly journals Image Forgery Detection and Localization using Modified JPEG Ghost

2019 ◽  
Vol 8 (12) ◽  
pp. 5433-5436
Keyword(s):  
Digital Image ◽  
Original Structure ◽  
Forgery Detection ◽  
Image Forgery ◽  
Digital Devices ◽  
Image Forgery Detection ◽  
Storage Format ◽  
Digital Image Forgery ◽  
Detection And Localization ◽  
Multiple Compression

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.

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

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 286
Author(s):  
Younis Abdalla ◽  
M. Tariq Iqbal ◽  
Mohamed Shehata
Keyword(s):  
Neural Network ◽  
New Technologies ◽  
Generative Adversarial Networks ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Generative Adversarial Network ◽  
Neural Network Approach ◽  
Adversarial Network ◽  
Copy Move Forgery Detection ◽  
Detection And Localization

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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