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Image blind detection based on LBP residue classes and color regions

Forgery detection is essential to verify the integrity and authenticity of images. Existing block-based detection techniques detect forgery in the same image, most of which use similar frameworks while differ in the feature extraction schemes. These methods have high accuracy in detecting the forged...

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Detalles Bibliográficos
Autores principales: Zhu, Tingge, Zheng, Jiangbin, Lai, Yi, Liu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715236/
https://www.ncbi.nlm.nih.gov/pubmed/31465479
http://dx.doi.org/10.1371/journal.pone.0221627
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author Zhu, Tingge
Zheng, Jiangbin
Lai, Yi
Liu, Ying
author_facet Zhu, Tingge
Zheng, Jiangbin
Lai, Yi
Liu, Ying
author_sort Zhu, Tingge
collection PubMed
description Forgery detection is essential to verify the integrity and authenticity of images. Existing block-based detection techniques detect forgery in the same image, most of which use similar frameworks while differ in the feature extraction schemes. These methods have high accuracy in detecting the forged regions, but the computational load is heavy when facing exhaustive search problems. This paper describes a forgery detection method based on local binary pattern residue classes and color regions. An image is divided into overlapped blocks. Local binary pattern residue classes are computed for each block. The plane formed by a dimensional and b dimensional from Lab color space is divided into 16 regions. Similar blocks are searched in the overlapped blocks with the same local binary pattern residue class and color region, then they are grouped into several suspicious regions. Finally, we analyze the multi-region relation of these suspicious regions and their areas to locate the tampered regions. The small hole is filled through the morphologic operation. The results of experiments demonstrated that our method has good performance in that it improved detection accuracy and reduced execution time under various challenging conditions. As the proposed method reduces the search range for similar blocks, it has a higher speed than exhaustive search and has comparable detection results at the same time.
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spelling pubmed-67152362019-09-10 Image blind detection based on LBP residue classes and color regions Zhu, Tingge Zheng, Jiangbin Lai, Yi Liu, Ying PLoS One Research Article Forgery detection is essential to verify the integrity and authenticity of images. Existing block-based detection techniques detect forgery in the same image, most of which use similar frameworks while differ in the feature extraction schemes. These methods have high accuracy in detecting the forged regions, but the computational load is heavy when facing exhaustive search problems. This paper describes a forgery detection method based on local binary pattern residue classes and color regions. An image is divided into overlapped blocks. Local binary pattern residue classes are computed for each block. The plane formed by a dimensional and b dimensional from Lab color space is divided into 16 regions. Similar blocks are searched in the overlapped blocks with the same local binary pattern residue class and color region, then they are grouped into several suspicious regions. Finally, we analyze the multi-region relation of these suspicious regions and their areas to locate the tampered regions. The small hole is filled through the morphologic operation. The results of experiments demonstrated that our method has good performance in that it improved detection accuracy and reduced execution time under various challenging conditions. As the proposed method reduces the search range for similar blocks, it has a higher speed than exhaustive search and has comparable detection results at the same time. Public Library of Science 2019-08-29 /pmc/articles/PMC6715236/ /pubmed/31465479 http://dx.doi.org/10.1371/journal.pone.0221627 Text en © 2019 Zhu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhu, Tingge
Zheng, Jiangbin
Lai, Yi
Liu, Ying
Image blind detection based on LBP residue classes and color regions
title Image blind detection based on LBP residue classes and color regions
title_full Image blind detection based on LBP residue classes and color regions
title_fullStr Image blind detection based on LBP residue classes and color regions
title_full_unstemmed Image blind detection based on LBP residue classes and color regions
title_short Image blind detection based on LBP residue classes and color regions
title_sort image blind detection based on lbp residue classes and color regions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715236/
https://www.ncbi.nlm.nih.gov/pubmed/31465479
http://dx.doi.org/10.1371/journal.pone.0221627
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