research-article

Copy-Move Tampering Detection based on Local Binary Pattern Histogram Fourier Feature

Authors:

Dalton Meitei ThounaojamAuthors Info & Claims

ICCCT-2017: Proceedings of the 7th International Conference on Computer and Communication Technology

Pages 78 - 83

https://doi.org/10.1145/3154979.3155001

Published: 24 November 2017 Publication History

Abstract

Copy-move is a popular image tampering technique, where one or more regions of an image are copied and pasted into another portion of the same image with an objective to cover a conceivably important region or duplicate some regions. In this paper, a block-based blind technique for copy-move tampering detection is given by extracting Local Binary Pattern Histogram Fourier Features from each overlapping block. Proposed method is tested on benchmarking CoMoFoD dataset. Experimental results show that proposed method not only reduces the time complexity of tampering detection but also robust against different post-processing attacks such as blurring, brightness change, contrast adjustment etc.

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Cited By

Xue YZhu CTan X(2022)ISD-SSD: image splicing detection by using modified single shot MultiBox detectorInternational Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)10.1117/12.2659381(25)Online publication date: 30-Nov-2022
https://doi.org/10.1117/12.2659381
Sadu CDas P(2022)A Detection Method for Copy-Move Forgery Attacks in Digital ImagesTENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)10.1109/TENCON55691.2022.9977490(1-6)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TENCON55691.2022.9977490
Kumar NMeenpal T(2022)Salient keypoint-based copy–move image forgery detectionAustralian Journal of Forensic Sciences10.1080/00450618.2021.201696455:3(331-354)Online publication date: 12-Jan-2022
https://doi.org/10.1080/00450618.2021.2016964
Show More Cited By

Index Terms

Copy-Move Tampering Detection based on Local Binary Pattern Histogram Fourier Feature
1. Applied computing
  1. Computer forensics
    1. System forensics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Other conferences

ICCCT-2017: Proceedings of the 7th International Conference on Computer and Communication Technology

November 2017

157 pages

ISBN:9781450353243

DOI:10.1145/3154979

Copyright © 2017 ACM.

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

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Publication History

Published: 24 November 2017

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ICCCT-2017

ICCCT-2017: International Conference on Computer and Communication Technology

November 24 - 26, 2017

Allahabad, India

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ICCCT-2017 Paper Acceptance Rate 33 of 124 submissions, 27%;

Overall Acceptance Rate 33 of 124 submissions, 27%

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Cited By

Xue YZhu CTan X(2022)ISD-SSD: image splicing detection by using modified single shot MultiBox detectorInternational Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)10.1117/12.2659381(25)Online publication date: 30-Nov-2022
https://doi.org/10.1117/12.2659381
Sadu CDas P(2022)A Detection Method for Copy-Move Forgery Attacks in Digital ImagesTENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)10.1109/TENCON55691.2022.9977490(1-6)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TENCON55691.2022.9977490
Kumar NMeenpal T(2022)Salient keypoint-based copy–move image forgery detectionAustralian Journal of Forensic Sciences10.1080/00450618.2021.201696455:3(331-354)Online publication date: 12-Jan-2022
https://doi.org/10.1080/00450618.2021.2016964
Kalyan MKishore DSingh M(2022)Local Binary Pattern Symmetric Centre Feature Extraction Method for Detection of Image ForgeryArtificial Intelligence and Data Science10.1007/978-3-031-21385-4_8(89-100)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-21385-4_8
Mathur PSoni B(2021)Exploring Color Models for Enhancement of Underwater ImageData Driven Approach Towards Disruptive Technologies10.1007/978-981-15-9873-9_26(325-336)Online publication date: 7-Apr-2021
https://doi.org/10.1007/978-981-15-9873-9_26
Yao HXu MQiao TWu YZheng N(2020)Image Forgery Detection and Localization via a Reliability Fusion MapSensors10.3390/s2022666820:22(6668)Online publication date: 21-Nov-2020
https://doi.org/10.3390/s20226668
Soni BMathur P(2020)An Improved Image Dehazing Technique using CLAHE and Guided Filter2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN48934.2020.9071296(902-907)Online publication date: Feb-2020
https://doi.org/10.1109/SPIN48934.2020.9071296
Arun Anoop MPoonkuntran S(2020)RETRACTED ARTICLE: LPG: a novel approach for medical forgery detection in image transmissionJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-01932-012:5(4925-4941)Online publication date: 18-Apr-2020
https://doi.org/10.1007/s12652-020-01932-0
Soni BDas PThounaojam D(2019)Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local featuresJournal of Information Security and Applications10.1016/j.jisa.2019.01.00745:C(44-51)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1016/j.jisa.2019.01.007
Soni BDas PThounaojam DBiswas D(2019)Copy–Move Attack Detection from Digital Images: An Image Forensic ApproachSmart Computing Paradigms: New Progresses and Challenges10.1007/978-981-13-9683-0_8(69-76)Online publication date: 1-Dec-2019
https://doi.org/10.1007/978-981-13-9683-0_8
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