Fragility analysis of adaptive quantization-based image hashing

Guopu ZHU, Jiwu HUANG, Sam KWONG, Jianquan YANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

24 Citations (Scopus)


Fragility is one of the most important properties of authentication- oriented image hashing. However, to date, there has been little theoretical analysis on the fragility of image hashing. In this paper, we propose a measure called expected discriminability for the fragility of image hashing and study this fragility theoretically based on the proposed measure. According to our analysis, when Gray code is applied into the discrete-binary conversion stage of image hashing, the value of the expected discriminability, which is dominated by the quantization stage of image hashing, is no more than 1/2. We further evaluate the expected discriminability of the image-hashing scheme that uses adaptive quantization, which is the most popular quantization scheme in the field of image hashing. Our evaluation reveals that if deterministic adaptive quantization is applied, then the expected discriminability of the image-hashing scheme can reach the maximum value (i.e., 1/2). Finally, some experiments are conducted to validate our theoretical analysis and to compare the performance of several quantization schemes for image hashing. © 2010 IEEE.
Original languageEnglish
Pages (from-to)133-147
JournalIEEE Transactions on Information Forensics and Security
Issue number1
Publication statusPublished - Mar 2010
Externally publishedYes

Bibliographical note

This work was supported in part by NSFC under Grant 60633030, in part by the 973 Program under Grant 2006CB303104, in part by CPSF under Grant 20080430116, in part by City University under Strategic Grant 7002441, and in part by RGC General Research Fund 9041236 CityU 114707.


  • Adaptive quantization
  • Authentication
  • Fragility
  • Gray code
  • Image hashing
  • Robustness


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