Rihamark: Perceptual image hash benchmarking

Martin Steinebach, Christoph Zauner, Eckehard Hermann

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

24 Citations (Scopus)

Abstract

We identify which hash function has the best characteristics for various applications. In some of those the computation speed may be the most important, in others the ability to distinguish similar images, and sometimes the robustness of the hash against attacks is the primary goal. We compare the hash functions and provide test results. The block mean value based image hash function outperforms the other hash functions in terms of speed. The discrete cosine transform (DCT) based image hash function is the slowest. Although the Marr- Hildreth operator based image hash function is neither the fastest nor the most robust, it offers by far the best discriminative abilities. Interestingly enough, the performance in terms of discriminative ability does not depend on the content of the images. That is, no matter whether the visual appearance of the images compared was very similar or not, the performance of the particular hash function did not change significantly. Different image operations, like horizontal flipping, rotating or resizing, were used to test the robustness of the image hash functions. An interesting result is that none of the tested image hash function is robust against flipping an image horizontally.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Media Watermarking, Security, and Forensics III
PublisherSPIE Press
ISBN (Print)9780819484178
DOIs
Publication statusPublished - 2011
EventMedia Watermarking, Security, and Forensics III - San Francisco, CA, United States
Duration: 24 Jan 201126 Jan 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7880
ISSN (Print)0277-786X

Conference

ConferenceMedia Watermarking, Security, and Forensics III
Country/TerritoryUnited States
CitySan Francisco, CA
Period24.01.201126.01.2011

Keywords

  • benchmarking
  • evaluation
  • image fingerprint
  • Perceptual image hash
  • robust image hash

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