scholarly journals Copy-Move Forgery Detection in Digital Images using Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1560-1564
Keyword(s):  
Neural Network ◽  
Digital Images ◽  
Experimental Results ◽  
Image Editing ◽  
Forgery Detection ◽  
Software Applications ◽  
Key Points ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
Multiple Stages

Due to easy availability of image editing software applications, many of the digital images are tempered, either to hide some important facts of the image or just to enhance the image. Hence, the integrity of the image is compromised. Thus, in order to preserve the authenticity of an image, it is necessary to develop some algorithms to detect counterfeit parts of an image, if there is any. Two kinds of classic methods exist for the detection of forgery: the key- point based method in which major key points of the image is found and forged part is detected and the block based method that locates the forged part by sectioning the whole image into blocks. Unlike these two classic methods that require multiple stages, our proposed CNN solution provides better image forgery detection. Our experimental results revealed a better forgery detection performance than any other classic approaches.

Image Copy-Move Forgery Detection Based on SIFT and Gray Level

2012 ◽  
Vol 263-266 ◽  
pp. 3021-3024 ◽  
Author(s):  
Xuan Jing Shen ◽  
Ye Zhu ◽  
Ying Da Lv ◽  
Hai Peng Chen
Keyword(s):  
Feature Vector ◽  
Experimental Results ◽  
Gray Level ◽  
Forgery Detection ◽  
Improved Method ◽  
Sift Feature ◽  
Key Points ◽  
Gaussian Blur ◽  
Copy Move Forgery Detection ◽  
Improved Algorithm

In order to reduce the false matching rate when detecting copy-move forgeries, an improved method based on SIFT and gray level was proposed in this study. Firstly, extract SIFT key points, and establish SIFT feature vector for every key point; Secondly, extract the gray level feature and combine it with SIFT feature to found a feature vector with size of 129D; Finally, match the above feature vector between every two different key points and then the copy-move regions would be detected. The experimental results showed that the improved algorithm reduced false matching rate even when an image was distorted by Gaussian blur.

scholarly journals Nonoverlapping Blocks Based Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Sun ◽  
Rongrong Ni ◽  
Yao Zhao
Keyword(s):  
Computational Complexity ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Smooth Part ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
High Computational Complexity

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

scholarly journals Erratum to: An improved block-based matching algorithm of copy-move forgery detection

2017 ◽  
Vol 77 (12) ◽  
pp. 15111-15111
Author(s):  
Yuecong Lai ◽  
Tianqiang Huang ◽  
Jing Lin ◽  
Henan Lu
Keyword(s):  
Forgery Detection ◽  
Matching Algorithm ◽  
Block Based ◽  
Copy Move Forgery Detection

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.

A New Algorithm for Image Copy-Move Forgery Detection

2012 ◽  
Vol 433-440 ◽  
pp. 5930-5934 ◽  
Author(s):  
Dong Mei Hou ◽  
Zheng Yao Bai ◽  
Shu Chun Liu
Keyword(s):  
Image Forensics ◽  
Minimum Variance ◽  
Experimental Results ◽  
Phase Correlation ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Typical Distribution ◽  
Copy Move Forgery Detection ◽  
Minimum Variance Method ◽  
Application Scope

A new image forensics algorithm based on phase correlation is proposed to detect image copy-move forgery. Phase correlation is computed to obtain the typical distribution of correlation value and then minimum variance method is applied to determine the pulse diagram. The spatial offset between copied portion and pasted portion is estimated according to the pulse position, thus the copy-move region can be quickly located. Experimental results indicate that this method is not only implemented easily, but also achieve an effective and accurate location for small tampered areas. With this method, detection accuracy is guaranteed and application scope of the algorithm is extended simultaneously.

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