scholarly journals Copy Move Image Forgery Detection Using SIFT

2016 ◽  
Vol 9 (3) ◽  
pp. 235-245
Author(s):  
Priyanka Ruikar ◽  
Pravin Patil
Keyword(s):  
Digital Data ◽  
Forgery Detection ◽  
Digital Form ◽  
Scale Invariant ◽  
Important Data ◽  
Image Forgery ◽  
Local Characteristics ◽  
Invariant Feature ◽  
Points Of Interest ◽  
Scale Invariant Feature

In recent years the digital form of data allowing ease on to manipulation & storage due to progress in technology. But this progress in technology has lots of risks especially when it comes to the security of the digital data & files. Basically, image forgery means malfunctioning & playing with images or manipulating data fraudulently. In that case, some important data may get hidden in the original image. In particular, many organizations worry for digital forgery, because it is easier to create fake & fraudulent images without leaving any Tampering traces. A copy-move is a specific form of image forgery operation & it is considered one of the most difficult problems in that case for this a part of any image is copied & pa tested on another location of an image that may be a same or different image, to obfuscate undesirable objects in the scene. In this paper, the method is proposed which automatically detects & identifies the duplicated regions in the image. In that process first image segmentation takes place & by identifying the local characteristics of the images (points of interest) the duplication is detected using SIFT (Scale Invariant Feature Transform).

Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features

2021 ◽  
Author(s):  
Marziye Shahrokhi ◽  
Alireza Akoushideh ◽  
Asadollah Shahbahrami
Keyword(s):  
Local Binary Pattern ◽  
Digital Images ◽  
Scale Invariant Feature Transform ◽  
Combination Method ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.

scholarly journals Image Forgery Recognition using SWT Method

2019 ◽  
Vol 8 (6) ◽  
pp. 293-296
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Feature Transformation ◽  
Scale Invariant ◽  
Image Forgery ◽  
Invariant Feature ◽  
Scale Invariant Feature

Using SWT (Stationary Wavelet Change) & SIFT (Scale Invariant Feature Transformation) we attempted to increase the number of features recognized & matched with digital image for forgery identification. Digital image received preferable match for forged area. We collected the forgery area using SIFT& SURF for identification of forgery. We used DWT (Discrete Wavelet Transform) w.r.t. SIFT & SW to subdue absence of translation invariance..

scholarly journals Novel Copy Move Forgery Detection Based on Repeated Feature Extraction and Delaunay Triangulation

2020 ◽  
Vol 9 (3) ◽  
pp. 2364-2369
Keyword(s):  
Delaunay Triangulation ◽  
Feature Points ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Naked Eye ◽  
Invariant Feature ◽  
Random Sample Consensus ◽  
Feature Transform ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Nowadays new and creative methods of forging images are developed with the invention of sophisticated softwares like Adobe photoshop. Tools available in such softwares will make the forged image look real which cannot be even identified by a naked eye. In this paper, key point based approach of taking out features using Scale Invariant Feature Transform (SIFT) is used. The feature points thus extracted are then modeled to get a set of triangles using Delaunay Triangulation method. These triangles are matched using mean vertex descriptor and the removal of false positives is done using the method of Random Sample Consensus (RANSAC). Implementation show that the proposed approach outdoes the equivalent methods

Sign in / Sign up

Export Citation Format

Share Document