scholarly journals Copy-Move Forgery Detection Based on Pyramid Correlation Network

CONVERTER ◽  
2021 ◽  
pp. 745-755
Author(s):  
Peng Liang, Et al.
Keyword(s):  
Image Representation ◽  
Small Region ◽  
Spatial Layout ◽  
Correlation Network ◽  
Data Sets ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
The Poor ◽  
Block Based ◽  
Copy Move Forgery Detection

Block-based image copy-move detection algorithms disregard the spatial layout of the features, leading to the poor detection performance under small-region tampering samples. Therefore, we propose a pyramid correlation network (PCNet) for copy-move forgery detection, whose goal is to obtain rich and detailed image representation via a pyramid cascaded correlation architecture. Experimental results show that PCNet outperforms the comparison algorithm on USCISI, CASIA and CoMoFoD data sets. Compared to the benchmark model BusterNet, F1 scores of PCNet has increased by 33.84% and 30.62% on CASIA CMFD dataset and CoMoFoD dataset respectively.

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

A new block-based method for copy move forgery detection under image geometric transforms

2016 ◽  
Vol 76 (13) ◽  
pp. 14887-14903 ◽  
Author(s):  
Junliu Zhong ◽  
Yanfen Gan ◽  
Janson Young ◽  
Lian Huang ◽  
Peiyu Lin
Keyword(s):  
Forgery Detection ◽  
Block Based ◽  
Copy Move Forgery Detection

An improved block-based matching algorithm of copy-move forgery detection

2017 ◽  
Vol 77 (12) ◽  
pp. 15093-15110 ◽  
Author(s):  
Yuecong Lai ◽  
Tianqiang Huang ◽  
Jing Lin ◽  
Henan Lu
Keyword(s):  
Forgery Detection ◽  
Matching Algorithm ◽  
Block Based ◽  
Copy Move Forgery Detection

scholarly journals Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

2015 ◽  
Vol 24 (04) ◽  
pp. 1540016 ◽  
Author(s):  
Muhammad Hussain ◽  
Sahar Qasem ◽  
George Bebis ◽  
Ghulam Muhammad ◽  
Hatim Aboalsamh ◽  
...  
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Feature Space ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
Forgery Detection ◽  
Data Set ◽  
Multi Scale ◽  
Copy Move Forgery Detection

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

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