research-article

An improved camera identification method based on the texture complexity and the image restoration

Authors:

Kazuya Matsushita,

Hitoshi KitazawaAuthors Info & Claims

ICHIT '09: Proceedings of the 2009 International Conference on Hybrid Information Technology

Pages 171 - 175

https://doi.org/10.1145/1644993.1645024

Published: 27 August 2009 Publication History

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Abstract

The identification of source camera is useful to improve the capability of evidence in the digital image such as distinguish the photographer taking illegal images and adopting digital images as evidence of crime. Lukáš, et al. showed the method for source camera identification based on the correlation of PNU (pixel nonuniformity) noise. However, the wavelet-based denoising filter for suppressing the random noise reduces the accuracy of camera identification. It is caused by the fact that the denoising filter diffuses the edge and makes the PNU noise less pronounced. Moreover, it is difficult to extract PNU noise from the images taken by cameras which are equipped with the image improvement functions such as motion blur correction, contrast enhancement, and noise reduction. In this paper, we propose a method for improving the camera identification accuracy by selecting pixels based on the texture complexity. We also propose a-method for improving the identification accuracy by applying the image restoration method.

References

[1]

J Lukas, et al. "Digital Camera Identification from Sensor Pattern Noise", IEEE Trans. IFS, June 2006

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[2]

Mihcak M. K., et al.: "Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Mar. 1999

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Cited By

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Li JLiu XLiu YHuo Z(2022)Intelligent data-driven denoising based on texture complexityJournal of Geophysics and Engineering10.1093/jge/gxac03919:3(578-593)Online publication date: 27-Jun-2022
https://doi.org/10.1093/jge/gxac039
de Roos LGeradts Z(2021)Factors that Influence PRNU-Based Camera-Identification via VideosJournal of Imaging10.3390/jimaging70100087:1(8)Online publication date: 13-Jan-2021
https://doi.org/10.3390/jimaging7010008
Zhang WLiu YZhou JYang YLaw N(2020)An Improved Sensor Pattern Noise Estimation Method Based on Edge Guided Weighted AveragingMachine Learning for Cyber Security10.1007/978-3-030-62460-6_36(405-415)Online publication date: 11-Nov-2020
https://doi.org/10.1007/978-3-030-62460-6_36
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Index Terms

An improved camera identification method based on the texture complexity and the image restoration
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
      2. Computer vision tasks
  2. Computer graphics
    1. Image manipulation

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Information

Published In

ICHIT '09: Proceedings of the 2009 International Conference on Hybrid Information Technology

August 2009

687 pages

ISBN:9781605586625

DOI:10.1145/1644993

General Chairs:
Geuk Lee
Hannam University, Korea
,
Daniel Howard
QinetiQ, UK
,
Jeong Jin Kang
Dong Seoul University, Korea
,
Program Chairs:
Dominik Slezak
Infobright, Canada
,
Tae Nam Ahn
SERC, Korea
,
Chung-Huang Yang
National Kaohsiung Normal University, Taiwan

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: 27 August 2009

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ICHIT '09

ICHIT '09: International Conference on Convergence and Hybrid Information Technology

August 27 - 29, 2009

Daejeon, Korea

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Cited By

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Li JLiu XLiu YHuo Z(2022)Intelligent data-driven denoising based on texture complexityJournal of Geophysics and Engineering10.1093/jge/gxac03919:3(578-593)Online publication date: 27-Jun-2022
https://doi.org/10.1093/jge/gxac039
de Roos LGeradts Z(2021)Factors that Influence PRNU-Based Camera-Identification via VideosJournal of Imaging10.3390/jimaging70100087:1(8)Online publication date: 13-Jan-2021
https://doi.org/10.3390/jimaging7010008
Zhang WLiu YZhou JYang YLaw N(2020)An Improved Sensor Pattern Noise Estimation Method Based on Edge Guided Weighted AveragingMachine Learning for Cyber Security10.1007/978-3-030-62460-6_36(405-415)Online publication date: 11-Nov-2020
https://doi.org/10.1007/978-3-030-62460-6_36
Bolouri KAzmoodeh ADehghantanha AFirouzmand M(2019)Internet of Things Camera Identification Algorithm Based on Sensor Pattern Noise Using Color Filter Array and Wavelet TransformHandbook of Big Data and IoT Security10.1007/978-3-030-10543-3_9(211-223)Online publication date: 23-Mar-2019
https://doi.org/10.1007/978-3-030-10543-3_9
Moon CKim HKim B(2014)Audio Recorder Identification Using Reduced Noise FeaturesUbiquitous Information Technologies and Applications10.1007/978-3-642-41671-2_6(35-42)Online publication date: 2014
https://doi.org/10.1007/978-3-642-41671-2_6
Shi CSu YZhang JXu J(2012)Sensor pattern noise in JPEG compressed images2012 International Conference on Audio, Language and Image Processing10.1109/ICALIP.2012.6376603(155-159)Online publication date: Jul-2012
https://doi.org/10.1109/ICALIP.2012.6376603

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