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Robust Hash Functions for Visual Data: An Experimental Comparison

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Pattern Recognition and Image Analysis (IbPRIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2652))

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Abstract

Robust hash functions for visual data need a feature extraction mechanism to rely on. We experimentally compare spatial and transform domain feature extraction techniques and identify the global DCT combined with the cryptographic hash function MD-5 to be suited for visual hashing. This scheme offers robustness against JPEG2000 and JPEG compression and qualitative sensitivity to intentional global and local image alterations.

This work has been partially supported by the Austrian Science Fund FWF, project no. P15170.

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References

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Authors and Affiliations

  1. Carinthia Tech Institute, School of Telematics & Network Engineering, Primoschgasse 8, A-9020, Klagenfurt, Austria

    Champskud J. Skrepth & Andreas Uhl

  2. Department of Scientific Computing, Salzburg University, Jakob-Haringerstr.2, A-5020, Salzburg, Austria

    Andreas Uhl

Authors
  1. Champskud J. Skrepth
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  2. Andreas Uhl
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Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco José Perales

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio J. C. Campilho

  3. Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  4. Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain

    Alberto Sanfeliu

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© 2003 Springer-Verlag Berlin Heidelberg

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Skrepth, C.J., Uhl, A. (2003). Robust Hash Functions for Visual Data: An Experimental Comparison. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_114

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  • DOI: https://doi.org/10.1007/978-3-540-44871-6_114

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40217-6

  • Online ISBN: 978-3-540-44871-6

  • eBook Packages: Springer Book Archive

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