IEEE Transactions on Information Forensics and Security,
vol. 1, no. 1, pp. 68-79, Mar. 2006.
A Clustering Based Approach to 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, and
authentication. In this paper, we decouple image hashing into
feature extraction (intermediate hash) followed by data clustering
(final hash). We show that the decision version of our clustering
problem is NP complete. Then, 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. Based on the
proposed algorithm, we develop two variations to facilitate
perceptual robustness vs. fragility trade-offs. We validate the
perceptual significance of our hash by testing under Stirmark
attacks. Finally, we develop randomized clustering algorithms for
the purposes of secure image hashing.
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