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Comparative Compression Robustness Evaluation of Digital Image Forensics

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

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Abstract

The robustness of two important digital image forensic tasks (i.e. SIFT-based copy-move forgery detection and PRNU-based camera sensor identification) against four different lossy compression techniques is investigated (while typically, only JPEG compression is considered) to identify the best suited technique for this application scenario. Overall, we find that the accuracy of forensic tasks is reduced for increasing compression strength as expected, however, the relative performance of the compression schemes is different for the two tasks. While JPEG is superior for realistic application settings (where accuracy is in an acceptable range) in SIFT-based copy-move forgery detection, JPEG XR and BPG provide the best option for PRNU-based camera sensor identification, whereas JPEG is clearly impacting this forensic application most severely.

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Notes

  1. 1.

    https://bellard.org/bpg/.

  2. 2.

    http://opencv.org/.

  3. 3.

    https://dde.binghamton.edu/download/camera_fingerprint/.

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Acknowledgments

The authors gratefully acknowlege the contribution of the further Media Data Formats Lab students Max Göttl, Christoph Schwengler, Dennis Strähhuber, Benedikt Streitwieser, and Tim Ungerhofer. This work has been partially supported by the Salzburg State Government project “Artificial Intelligence in Industrial Vision Salzburg”.

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Authors and Affiliations

  1. Visual Computing and Security Lab (VISEL), Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, Austria

    Oliver Remy, Sebastian Strumegger, Jutta Hämmerle-Uhl & Andreas Uhl

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  1. Oliver Remy
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  2. Sebastian Strumegger
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  3. Jutta Hämmerle-Uhl
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  4. Andreas Uhl
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Corresponding author

Correspondence to Andreas Uhl .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Remy, O., Strumegger, S., Hämmerle-Uhl, J., Uhl, A. (2022). Comparative Compression Robustness Evaluation of Digital Image Forensics. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_19

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  • DOI: https://doi.org/10.1007/978-3-031-10450-3_19

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