Quaternion-Based Image Hashing for Adaptive Tampering Localization

Cai Ping Yan, Chi Man Pun, Xiao Chen Yuan

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)

Abstract

Image-hashing-based tampering detection methods have been widely studied with continuous advancements. However, most of existing models are designed for a specific tampering. In this paper, we propose a novel quaternion-based image hashing to detect almost all types of tampering, including color changing, copy move, splicing, and so on. First, the quaternion Fourier-Mellin transform is used to calculate the geometric hash to eliminate the influence of geometric distortions. Then, a new quaternion image construction method, which combines advantages of both color and structural features, is proposed to implement the quaternion Fourier transform to calculate the image feature hash to locate the tampered regions. The objective is to provide a reasonably short image hashing with good performance, i.e., being perceptually robust against various content-preserving attacks while capable of detecting and locating almost all types of tampering. Furthermore, an adaptive tampering localization algorithm is proposed based on clustering analysis to improve the detection accuracy. The experimental results show that the proposed tampering detection model outperforms the existing state-of-the-art models and is very robust against various content-preserving attacks.

Original languageEnglish
Article number7523392
Pages (from-to)2664-2677
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 2016
Externally publishedYes

Keywords

  • Adaptive tampering localization
  • Image hashing
  • quaternion Fourier transform (QFT)
  • quaternion Fourier-Mellin transform (QMMT)

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