Clustering algorithms for perceptual image hashing

Vishal Monga, Arindam Banerjee, Brian L. Evans

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2004 IEEE 11th Digital Signal Processing Workshop and 2nd IEEE Signal Processing Education Workshop
Pages283-287
Number of pages5
StatePublished - 2004
Event2004 IEEE 11th Digital Signal Processing Workshop and 2nd IEEE Signal Processing Education Workshop - Taos Ski Valley, NM, United States
Duration: Aug 1 2004Aug 4 2004

Other

Other2004 IEEE 11th Digital Signal Processing Workshop and 2nd IEEE Signal Processing Education Workshop
CountryUnited States
CityTaos Ski Valley, NM
Period8/1/048/4/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Monga, V., Banerjee, A., & Evans, B. L. (2004). Clustering algorithms for perceptual image hashing. In 2004 IEEE 11th Digital Signal Processing Workshop and 2nd IEEE Signal Processing Education Workshop (pp. 283-287)