Proc. IEEE Work. on Digital Signal Processing,
Aug. 1-4, 2004, pp. 283-287, Taos, NM.
Clustering Algorithms for Perceptual Image Hashing
Arindam Banerjee, and
Brian L. Evans
Center for Perceptual Systems,
The University of Texas at Austin,
Austin, TX 78712 USA
Image Hashing Research
at UT Austin
A perceptual image hash function maps an image to a short binary string
based on an image's appearance to the human eye.
Perceptual image hashing is useful in image databases, watermarking,
In this paper, we decouple image hashing into feature extraction
(intermediate hash) followed by data clustering (final hash).
For any perceptually significant feature extractor, we propose a
polynomial-time heuristic clustering algorithm that automatically
determines the final hash length needed to satisfy a specified distortion.
We prove that the decision version of our clustering problem is NP
Based on the proposed algorithm, we develop two variations to facilitate
perceptual robustness vs. fragility trade-offs.
We test the proposed algorithms against Stirmark attacks.
COPYRIGHT NOTICE: All the documents on this server
have been submitted by their authors to scholarly journals or conferences
as indicated, for the purpose of non-commercial dissemination of
The manuscripts are put on-line to facilitate this purpose.
These manuscripts are copyrighted by the authors or the journals in which
they were published.
You may copy a manuscript for scholarly, non-commercial purposes, such
as research or instruction, provided that you agree to respect these
Last Updated 07/17/06.