A benchmark for perceptual hashing based on human subjective identification

Hui Zhang, Qiong Li, Haibin Zhang, Xiamu Niu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study proposed a novel benchmark for evaluating the robustness and discriminabiliry properties of perceptual hashing algorithms. Firstly, two major problems neglected by traditional benchmark are analyzed thoroughly with a concrete experiment. One problem is the inconsistence between the subjective feeling and the objective perceptual distance, the other is the partiality of the performance for different attacks. And then, in order to overcome the problems, a new benchmark for perceptual hashing based on human subjective identification is proposed and the corresponding evaluation methods are presented by illustrative experiments and examples. Present benchmark methods are fairer and more comprehensive than the traditional methods.

Original languageEnglish
Pages (from-to)544-550
Number of pages7
JournalInformation Technology Journal
Volume8
Issue number4
DOIs
Publication statusPublished - 2009
Externally publishedYes

Keywords

  • Benchmark
  • CBIR
  • Discriminability
  • Perceptual hashing
  • Robustness
  • Subjective identification

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