scholarly journals Copy move forgery detection using key point localized super pixel based on texture features

2019 ◽  
Vol 43 (2) ◽  
pp. 270-276
Author(s):  
C. Rajalakshmi ◽  
Al.M. Germanus ◽  
R. Balasubramanian
Keyword(s):  
Detection Method ◽  
Texture Features ◽  
Experimental Result ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Image Forensic ◽  
Important Barrier ◽  
Copy Move Forgery Detection ◽  
Yield Good Result ◽  
Scale Invariant Feature

The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.

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.

Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform

2015 ◽  
Vol 4 (3) ◽  
pp. 70-89
Author(s):  
Ramesh Chand Pandey ◽  
Sanjay Kumar Singh ◽  
K K Shukla
Keyword(s):  
Image Features ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Hybrid Features ◽  
Single Region ◽  
Multiple Regions ◽  
Speed Up ◽  
Common Technique ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.

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

scholarly journals Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 492 ◽  
Author(s):  
Jun Young Park ◽  
Tae An Kang ◽  
Yong Ho Moon ◽  
Il Kyu Eom
Keyword(s):  
Local Binary Pattern ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Region Duplication ◽  
Invariant Feature ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

Laser Printing Files Detection Method Based on Double Features

2018 ◽  
Vol 32 (10) ◽  
pp. 1854028
Author(s):  
Juan Zhu ◽  
Jipeng Huang ◽  
Lianming Wang
Keyword(s):  
Detection Method ◽  
Texture Features ◽  
Image Features ◽  
Scale Invariant ◽  
Laser Printing ◽  
Valid Data ◽  
Sift Features ◽  
Low Efficiency ◽  
Original File ◽  
Histogram Feature

A novel laser printing files detection method is proposed in this paper to solve the problem of low efficiency and difficulty in traditional detection. The new method is based on improved scale-invariant feature transform (SIFT) feature and histogram feature. Firstly, analyze the graphical features of different laser printing files. Different files have different printing texture features in valid data area. So segment the valid data area to remove the interference of background. Secondly, extract the histogram feature of the same character in the printing file. Normalize the histogram and then calculate the Bhattacharyya coefficient between the detected file and the original file to determine whether the detected file is right or fake. At the same time, calculate the SIFT features and match the detected file and the original file. To focus on the letter or character region, the SIFT features which are out of contour are deleted. Finally, the results of the two different methods are both used as the result of the identification. When any of the result is fake, the end result will be fake. In the self-built database experiment, in different printing files from different printers, the inkjet areas possess different image features. When scanning different files using 600 dpi, the detect accuracy is higher than 97%. This method was able to meet the reliability requirements of law.

Sign in / Sign up

Export Citation Format

Share Document