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LSSD: A Controlled Large JPEG Image Database for Deep-Learning-Based Steganalysis “Into the Wild”

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12666))

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Abstract

For many years, the image databases used in steganalysis have been relatively small, i.e. about ten thousand images. This limits the diversity of images and thus prevents large-scale analysis of steganalysis algorithms.

In this paper, we describe a large JPEG database composed of 2 million colour and grey-scale images. This database, named LSSD for Large Scale Steganalysis Database, was obtained thanks to the intensive use of “controlled” development procedures. LSSD has been made publicly available, and we aspire it could be used by the steganalysis community for large-scale experiments.

We introduce the pipeline used for building various image database versions. We detail the general methodology that can be used to redevelop the entire database and increase even more the diversity. We also discuss computational cost and storage cost in order to develop images.

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Notes

  1. 1.

    Website of the ALASKA challenge#2: https://alaska.utt.fr/ Download page: http://alaska.utt.fr/ALASKA_v2_RAWs_scripts.zip.

  2. 2.

    Challenge BOSS: http://agents.fel.cvut.cz/boss/index.php?mode=VIEW&tmpl=about Download page: ftp://mas22.felk.cvut.cz/RAWs .

  3. 3.

    Obsolete download link http://mmlab.science.unitn.it/RAISE/.

  4. 4.

    http://forensics.inf.tu-dresden.de/ddimgdb Download page: http://forensics.inf.tu-dresden.de/ddimgdb/selections .

  5. 5.

    Site closed on February 17, 2020: http://wesaturate.com/.

  6. 6.

    https://data.csafe.iastate.edu/StegoDatabase/.

  7. 7.

    Documentation: https://pillow.readthedocs.io/en/stable/.

  8. 8.

    Software available at: http://rawtherapee.com More information can be found at: http://rawpedia.rawtherapee.com.

  9. 9.

    Documentation about the different mosaicking methods of Rawtherapee can be found at: https://rawpedia.rawtherapee.com/Demosaicing.

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Acknowledgment

The authors would like to thank the French Defense Procurement Agency (DGA) for its support through the ANR Alaska project (ANR-18-ASTR-0009). We also thank IBM Montpellier and the Institute for Development and Resources in Intensive Scientific Computing (IDRISS/CNRS) for providing us access to High-Performance Computing resources.

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Authors and Affiliations

  1. Research-Team ICAR, LIRMM, Univ. Montpellier, CNRS, Montpellier, France

    Hugo Ruiz, Mehdi Yedroudj, Marc Chaumont, Frédéric Comby & Gérard Subsol

  2. University of Nîmes, Nîmes, France

    Marc Chaumont

Authors
  1. Hugo Ruiz
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  2. Mehdi Yedroudj
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  3. Marc Chaumont
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  4. Frédéric Comby
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  5. Gérard Subsol
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Corresponding author

Correspondence to Marc Chaumont .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Ruiz, H., Yedroudj, M., Chaumont, M., Comby, F., Subsol, G. (2021). LSSD: A Controlled Large JPEG Image Database for Deep-Learning-Based Steganalysis “Into the Wild”. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_38

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  • DOI: https://doi.org/10.1007/978-3-030-68780-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68779-3

  • Online ISBN: 978-3-030-68780-9

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