Histogram-based image hashing for searching content-preserving copies

Shijun Xiang, Hyong Joong Kim

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Image hashing as a compact abstract can be used for content search. Towards this end, a desired image hashing function should be resistant to those content-preserving manipulations (including additive-noise like processing and geometric deformation operations). Most countermeasures proposed in the literature usually focus on the problem of additive noises and global affine transform operations, but few are resistant to recently reported random bending attacks (RBAs). In this paper, we address an efficient and effective image hashing algorithm by using the resistance of two statistical features (image histogram in shape and mean value) for those challenging geometric deformations. Since the features are extracted from Gaussian-filtered images, the hash is also robust to common additive noise-like operations (e.g., lossy compression, low-pass filtering). The hash uniqueness is satisfactory for different sources of images. With a large number of real-world images, we construct a hash-based image search system to show that the hash function can be used for searching content-preserving copies from the same source.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages83-108
Number of pages26
Volume6730 LNCS
DOIs
Publication statusPublished - 2011 Dec 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6730 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Fingerprint

Additive noise
Hashing
Histogram
Affine transforms
Additive Noise
Hash functions
Processing
Lossy Compression
Hash Function
Countermeasures
Mean Value
Manipulation
Uniqueness
Filtering
Attack
Transform

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Xiang, S., & Kim, H. J. (2011). Histogram-based image hashing for searching content-preserving copies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6730 LNCS, pp. 83-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6730 LNCS). https://doi.org/10.1007/978-3-642-24556-5_5

Histogram-based image hashing for searching content-preserving copies. / Xiang, Shijun; Kim, Hyong Joong.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6730 LNCS 2011. p. 83-108 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6730 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapter

Xiang, S & Kim, HJ 2011, Histogram-based image hashing for searching content-preserving copies. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6730 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6730 LNCS, pp. 83-108. https://doi.org/10.1007/978-3-642-24556-5_5
Xiang S, Kim HJ. Histogram-based image hashing for searching content-preserving copies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6730 LNCS. 2011. p. 83-108. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-24556-5_5
Xiang, Shijun ; Kim, Hyong Joong. / Histogram-based image hashing for searching content-preserving copies. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6730 LNCS 2011. pp. 83-108 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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