research-article

A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation

Authors:

Yizhen HuangAuthors Info & Claims

MM&Sec '09: Proceedings of the 11th ACM workshop on Multimedia and security

Pages 125 - 130

https://doi.org/10.1145/1597817.1597839

Published: 07 September 2009 Publication History

Abstract

Demosaicked images possess spatially periodic inter-pixel correlation, because interpolation strategies at any logically equivalent spatial pixel location are identical. Taking this statistical characteristic, much research on imaging forensics has been done recently. We proposed a generalized neural network framework to simulate the stylized computational rules in demosaicking through bias and weight value adjustment. As experiments show, our framework is effective in recognizing the demosaicking algorithms for raw CFA images, as well as digital photo authentication, compared to the state-of-the-art methods.

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Cited By

Priyadharsini SDevi K(2022)Effective image splicing detection using deep neural networkInternational Journal of Wavelets, Multiresolution and Information Processing10.1142/S021969132250051521:02Online publication date: 19-Dec-2022
https://doi.org/10.1142/S0219691322500515
Gupta SMohan NKumar M(2020)A Study on Source Device Attribution Using Still ImagesArchives of Computational Methods in Engineering10.1007/s11831-020-09452-yOnline publication date: 8-Jun-2020
https://doi.org/10.1007/s11831-020-09452-y
Bammey QGrompone von Gioi RMorel J(2018)Automatic Detection of Demosaicing Image Artifacts and Its Use in Tampering Detection2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00091(424-429)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00091
Show More Cited By

Index Terms

A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation
1. Security and privacy
  1. Security services
    1. Authentication

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Published In

cover image ACM Conferences

MM&Sec '09: Proceedings of the 11th ACM workshop on Multimedia and security

September 2009

186 pages

ISBN:9781605584928

DOI:10.1145/1597817

General Chair:
Edward W. Felten
Center for Information Technology Policy, Princeton University USA
,
Program Chairs:
Jana Dittmann
Otto-von-Guericke University Magdeburg, Germany
,
Jessica Fridrich
SUNY Binghamton, USA
,
Scott Craver
SUNY Binghamton, USA

Copyright © 2009 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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Publication History

Published: 07 September 2009

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MM&Sec '09

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MM&Sec '09: Multimedia and Security Workshop

September 7 - 8, 2009

New Jersey, Princeton, USA

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Citations

Cited By

Priyadharsini SDevi K(2022)Effective image splicing detection using deep neural networkInternational Journal of Wavelets, Multiresolution and Information Processing10.1142/S021969132250051521:02Online publication date: 19-Dec-2022
https://doi.org/10.1142/S0219691322500515
Gupta SMohan NKumar M(2020)A Study on Source Device Attribution Using Still ImagesArchives of Computational Methods in Engineering10.1007/s11831-020-09452-yOnline publication date: 8-Jun-2020
https://doi.org/10.1007/s11831-020-09452-y
Bammey QGrompone von Gioi RMorel J(2018)Automatic Detection of Demosaicing Image Artifacts and Its Use in Tampering Detection2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00091(424-429)Online publication date: Apr-2018
https://doi.org/10.1109/MIPR.2018.00091
Singh ASingh GSingh K(2018)A Markov based image forgery detection approach by analyzing CFA artifactsMultimedia Tools and Applications10.1007/s11042-018-6075-577:21(28949-28968)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s11042-018-6075-5
Rota PSangineto EConotter VPramerdorfer C(2016)Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?2016 23rd International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2016.7900012(2503-2508)Online publication date: Dec-2016
https://doi.org/10.1109/ICPR.2016.7900012
Kuznetsov AMyasnikov V(2015)Sequential computational procedure for remote sensing data forgery detectionPattern Recognition and Image Analysis10.1134/S105466181504013625:4(645-653)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1134/S1054661815040136
Li LXue JWang XTian L(2015)A robust approach to detect digital forgeries by exploring correlation patternsPattern Analysis & Applications10.1007/s10044-013-0319-918:2(351-365)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1007/s10044-013-0319-9
Ferrara PBianchi TDe Rosa APiva A(2012)Image Forgery Localization via Fine-Grained Analysis of CFA ArtifactsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2012.22022277:5(1566-1577)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1109/TIFS.2012.2202227
Jin QHuang YFan N(2012)Learning images using compositional pattern-producing neural networks for source camera identification and digital demographic diagnosisPattern Recognition Letters10.1016/j.patrec.2011.09.00133:4(381-396)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1016/j.patrec.2011.09.001
Huang YLi YFan N(2010)Robust Symbolic Dual-View Facial Expression Recognition With Skin WrinklesIEEE Transactions on Multimedia10.1109/TMM.2010.205279212:6(536-543)Online publication date: 1-Oct-2010
https://dl.acm.org/doi/10.1109/TMM.2010.2052792
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