An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

2021 ◽  
Vol 74 ◽  
pp. 102966
Author(s):  
Sreenivasu Tinnathi ◽  
G. Sudhavani
Keyword(s):  
Feature Extraction ◽  
Watershed Segmentation ◽  
Forgery Detection ◽  
Hybrid Feature Extraction ◽  
Copy Move Forgery Detection

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

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.

scholarly journals A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

2021 ◽  
Vol 22 (2) ◽  
pp. 1177
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Image Features ◽  
Basic Process ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Algorithms ◽  
The Past ◽  
Copy Move Forgery Detection ◽  
Digital Image Forgery

<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

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