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A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns

Authors : Chao-Lung Chou, Jen-Chun Lee

Published in: Security with Intelligent Computing and Big-data Services

Publisher: Springer International Publishing

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Abstract

Nowadays digital images are more and more easily to be modified or tampered intentionally by most people due to the rapid development of powerful image processing software. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. Copy-move is one of the typical image forgery methods, in which a part of an image is duplicated and used to replace another part of the same image at a different location. In this paper, we proposed a block-based passive detect copy-move forgery detection method based on local Gabor wavelets patterns (LGWP) with the advantages of high performance texture analysis of Gabor filter and rotation-invariant ability of uniform local binary pattern (LBP). Experiment results demonstrate the ability of the proposed method to detect copy-move forgery and precisely locate the duplicated regions, even when the forgery images are distorted by JPEG compression, blurring, brightness adjustment and rotation.

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Metadata
Title
Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns
Authors
Chao-Lung Chou
Jen-Chun Lee
Copyright Year
2018
Book
Security with Intelligent Computing and Big-data Services

Print ISBN: 978-3-319-76450-4

Electronic ISBN: 978-3-319-76451-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-76451-1_5

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