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.

Hybrid revised weighted fuzzy c-means clustering with Nelder-Mead simplex algorithm for generalized multisource Weber problem

2018 ◽  
Vol 31 (6) ◽  
pp. 908-924
Author(s):  
Tarik Kucukdeniz ◽  
Sakir Esnaf
Keyword(s):  
Cost Minimization ◽  
Simplex Algorithm ◽  
Data Sets ◽  
Weber Problem ◽  
Test Results ◽  
Content Type ◽  
Facility Location Problems ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
And Performance

PurposeThe purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).Design/methodology/approachAlthough the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.FindingsTest results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.Originality/valueProposed approach achieves better results in continuous facility location problems.

scholarly journals Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

2015 ◽  
Vol 24 (04) ◽  
pp. 1540016 ◽  
Author(s):  
Muhammad Hussain ◽  
Sahar Qasem ◽  
George Bebis ◽  
Ghulam Muhammad ◽  
Hatim Aboalsamh ◽  
...  
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Feature Space ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
Forgery Detection ◽  
Data Set ◽  
Multi Scale ◽  
Copy Move Forgery Detection

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

scholarly journals A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

2018 ◽  
Vol 7 (3) ◽  
pp. 345-349
Author(s):  
Anil Gupta
Keyword(s):  
Digital Image ◽  
Feature Point ◽  
Detection Methods ◽  
Forgery Detection ◽  
Point Matching ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Feature Point Matching ◽  
Copy Move Forgery Detection

With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

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