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Feature-Based Forensic Camera Model Identification

  • Conference paper
Transactions on Data Hiding and Multimedia Security VIII

Part of the book series: Lecture Notes in Computer Science ((TDHMS,volume 7228))

Abstract

State-of-the-art digital forensic techniques for camera model identification draw attention on different sets of features to assign an image to the employed source model. This paper complements existing work, by a comprehensive evaluation of known feature sets employing a large set of 26 camera models with altogether 74 devices. We achieved the highest accuracies using the extended colour feature set and present several detail experiments to validate the ability of the features to separate between camera models and not between devices. Analysing more than 16,000 images, we present a comprehensive evaluation on 1) the number of required images and devices for training, 2) the influence of the number of camera models and camera settings on the detection results and 3) possibilities to handle unknown camera models when not all models coming into question are available or are even known. All experiments in this paper suggest: feature-based forensic camera model identification works in practice and provides reliable results even if only one device for each camera model under investigation is available to the forensic investigator.

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Author information

Authors and Affiliations

  1. Institute of Systems Architecture, Technische Universität Dresden, 01062, Dresden, Germany

    Thomas Gloe

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  1. Thomas Gloe
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Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, 07102, Newark, NJ, USA

    Yun Q. Shi

  2. Department of Computer Science, Technical University Darmstadt, Hochschulstrasse 10, 64289, Darmstadt, Germany

    Stefan Katzenbeisser

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© 2012 Springer-Verlag Berlin Heidelberg

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Gloe, T. (2012). Feature-Based Forensic Camera Model Identification. In: Shi, Y.Q., Katzenbeisser, S. (eds) Transactions on Data Hiding and Multimedia Security VIII. Lecture Notes in Computer Science, vol 7228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31971-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-31971-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31970-9

  • Online ISBN: 978-3-642-31971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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