A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms

2020 ◽  
Vol 79 (11-12) ◽  
pp. 8197-8212 ◽  
Author(s):  
Kunj Bihari Meena ◽  
Vipin Tyagi
Keyword(s):  
Detection Technique ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Image Forgery ◽  
Invariant Feature ◽  
Image Forgery Detection ◽  
Scale Invariant Feature

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 CLIFD: A novel image forgery detection technique using digital signatures

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sahib Khan ◽  
◽  
Arslan Ali ◽  
Keyword(s):  
Digital Signature ◽  
Digital Signatures ◽  
Detection Technique ◽  
Forgery Detection ◽  
Image Forgery ◽  
Signature Generation ◽  
Image Forgery Detection ◽  
Substitution Mechanism ◽  
Different Types ◽  
Message Digest

The paper presents a new image forgery detection technique. The proposed technique uses digital signatures; it generates a digital signature for each column and embeds the signature in the least significant bits of each corresponding column’s selected pixels. The message digest algorithm 5 (MD5) is used for digital signature generation, and the fourleast-significant-bit substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.

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