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An Effective Training Strategy for Enhanced Source Camera Device Identification

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13646))

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Abstract

Source camera identification is a forensic problem of linking an image in question to the camera used to capture it. This could be a useful tool in forensic applications to identify potential suspects of cybercrime. Over the last decade, several successful attempts have been made to identify the source camera using deep learning. However, existing techniques that provide effective solutions for camera model identification fail to distinguish between different devices of the same model. This is because cameras of different brands and models were used to train the data-driven system when dealing with exact device identification. We show that training the data-driven system on different camera models opens side-channel information on model-specific features, which acts as interference for identifying individual devices of the same model. Thus, we provide an effective training strategy that involves a way to construct the dataset for enhanced source camera device identification. The experimental results suggest that involving only cameras of the same model for training improves the discriminative capability of the data-driven system by eliminating the threat of interfering model-specific features.

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Authors and Affiliations

  1. Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India

    Manisha & Karunakar A. Kotegar

  2. School of Information Technology, Deakin University, Geelong, 3216, Australia

    Chang-Tsun Li

Authors
  1. Manisha
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  2. Chang-Tsun Li
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  3. Karunakar A. Kotegar
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Corresponding author

Correspondence to Chang-Tsun Li .

Editor information

Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

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Manisha, Li, CT., Kotegar, K.A. (2023). An Effective Training Strategy for Enhanced Source Camera Device Identification. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13646. Springer, Cham. https://doi.org/10.1007/978-3-031-37745-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-37745-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37744-0

  • Online ISBN: 978-3-031-37745-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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