Copy-Move forgery detection in images using grey-level run length matrix features

2017 ◽  
Vol 3 (4) ◽  
pp. 303
Author(s):  
Saba Mushtaq ◽  
Ajaz Hussain Mir
Keyword(s):  
Grey Level ◽  
Forgery Detection ◽  
Run Length ◽  
Copy Move Forgery Detection

scholarly journals A Novel Technique for Copy Move Forgery Detection using Grey Level Co-occurrence Matrix

2019 ◽  
Vol 8 (9S) ◽  
pp. 581-586
Keyword(s):  
Feature Extraction ◽  
Grey Level ◽  
Forgery Detection ◽  
Novel Technique ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix

In this paper we have studied the GLCM approach as an improvement over SWT-DCT method for feature extraction for CMFD. We have carefully studied the previously used methods and also studied the SWT-DCT method for improvement in features. We have proposed a method for the use of GLCM instead of SWT-DCT method for feature extraction which will improve the results of CMFD method used in the base work framework.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Copy Move Forgery Detection based on double matching

2021 ◽  
Vol 76 ◽  
pp. 103057
Author(s):  
Qiyue Lyu ◽  
Junwei Luo ◽  
Ke Liu ◽  
Xiaolin Yin ◽  
Jiarui Liu ◽  
...  
Keyword(s):  
Forgery Detection ◽  
Copy Move Forgery Detection
Sign in / Sign up

Export Citation Format

Share Document