IEEE Transactions on Information Forensics and Security, vol. 1, no. 1, pp. 68-79, Mar. 2006.

A Clustering Based Approach to 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 - -

Paper - Software

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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Last Updated 07/17/06.