Proc. IEEE Work. on Digital Signal Processing,
Aug. 1-4, 2004, pp. 283-287, Taos, NM.
Clustering Algorithms for Perceptual Image Hashing
Vishal Monga,
Arindam Banerjee, and
Brian L. Evans
Center for Perceptual Systems,
The University of Texas at Austin,
Austin, TX 78712 USA
Vishal.Monga@xeroxlabs.com -
abanerje@ece.utexas.edu -
bevans@ece.utexas.edu
Paper -
Poster -
Software
Image Hashing Research
at UT Austin
Abstract
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,
and authentication.
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
complete.
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.
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