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Copy-move forgery detection via texture description

Authors:

Edoardo Ardizzone,

Alessandro Bruno,

Giuseppe MazzolaAuthors Info & Claims

MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence

Pages 59 - 64

https://doi.org/10.1145/1877972.1877990

Published: 29 October 2010 Publication History

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Abstract

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.

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Cited By

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Jothi JNithila EDavix X(2024)Region Duplication Tampering Detection and Localization in Digital Video Using Haar Wavelet TransformWireless Personal Communications10.1007/s11277-024-11028-z135:2(655-674)Online publication date: 8-May-2024
https://doi.org/10.1007/s11277-024-11028-z
Kashyap ATyagi KTyagi V(2023)Robust and Optimized Algorithm for Detection of Copy-Rotate-Move TemperingIEEE Access10.1109/ACCESS.2023.329112811(66626-66640)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3291128
Wang MFu XLiu JZha ZMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)JPEG Compression-aware Image Forgery LocalizationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3547749(5871-5879)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3547749
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Index Terms

Copy-move forgery detection via texture description
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations

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Published In

MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence

October 2010

134 pages

ISBN:9781450301572

DOI:10.1145/1877972

Program Chairs:
Sebastiano Battiato
University of Catania, Italy
,
Sabu Emmanuel
Nanyang Technological University, Singapore
,
Adrian Ulges
German Research Center for Artificial Intelligence, Germany
,
Marcel Worring
University of Amsterdam, The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 29, 2010

Firenze, Italy

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MM '24

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The 32nd ACM International Conference on Multimedia

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Cited By

View all

Jothi JNithila EDavix X(2024)Region Duplication Tampering Detection and Localization in Digital Video Using Haar Wavelet TransformWireless Personal Communications10.1007/s11277-024-11028-z135:2(655-674)Online publication date: 8-May-2024
https://doi.org/10.1007/s11277-024-11028-z
Kashyap ATyagi KTyagi V(2023)Robust and Optimized Algorithm for Detection of Copy-Rotate-Move TemperingIEEE Access10.1109/ACCESS.2023.329112811(66626-66640)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3291128
Wang MFu XLiu JZha ZMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)JPEG Compression-aware Image Forgery LocalizationProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3547749(5871-5879)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3547749
MURUGAN AARSATH A MANANDARAJ MNAVEENKUMAR M(2022)Similar Neighborhood Search and Key-Point Clustering in Forgery Detection2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC53929.2022.9792744(1375-1380)Online publication date: 9-May-2022
https://doi.org/10.1109/ICAAIC53929.2022.9792744
Manouchehri NBouguila NFan W(2022)Expectation propagation learning of finite multivariate Beta mixture models and applicationsNeural Computing and Applications10.1007/s00521-021-06839-134:17(14275-14285)Online publication date: 8-Jan-2022
https://doi.org/10.1007/s00521-021-06839-1
Soni BDas PSoni BDas P(2022)Background Study and AnalysisImage Copy-Move Forgery Detection10.1007/978-981-16-9041-9_2(11-31)Online publication date: 4-Feb-2022
https://doi.org/10.1007/978-981-16-9041-9_2
Bansal AAtri A(2020)Study of Copy-Move Forgery Detection Techniques in Images2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO48877.2020.9197801(630-635)Online publication date: Jun-2020
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Chen HYang XLyu Y(2020)Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search AlgorithmIEEE Access10.1109/ACCESS.2020.29748048(36863-36875)Online publication date: 2020
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Kashyap ASuresh BGupta H(2020)Robust Detection of Copy-Move Forgery Based on Wavelet Decomposition and Firefly AlgorithmThe Computer Journal10.1093/comjnl/bxaa13765:4(983-996)Online publication date: 7-Dec-2020
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Singhal SRanga V(2020)Passive Authentication Image Forgery Detection Using Multilayer CNNMobile Radio Communications and 5G Networks10.1007/978-981-15-7130-5_18(237-249)Online publication date: 29-Sep-2020
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