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 language||English (US)|
|Number of pages||12|
|Journal||IEEE Transactions on Information Forensics and Security|
|State||Published - Mar 2006|
Bibliographical noteFunding Information:
Manuscript received June 6, 2004; revised July 22, 2005. This work was supported by a grant from the Xerox Foundation. This work was performed while V. Monga and A. Banerjee were with the Department of Electrical and Computer Engineering, The University of Texas, Austin. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ton Kalker.
- Clustering based approach
- Feature extraction
- NP complete
- Perceptual image hashing
- Polynomial-time heuristic clustering