A clustering based approach to perceptual image hashing

Vishal Monga, A. Banerjee, B. L. Evans

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


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 versus fragility tradeoffs. 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.

Original languageEnglish (US)
Pages (from-to)68-79
Number of pages12
JournalIEEE Transactions on Information Forensics and Security
Issue number1
StatePublished - Mar 2006

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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