Copy-move forgery detection using on locality sensitive hashing and k-means clustering

Osamah M. Al-Qershi, Bee Ee Khoo

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11 Citations (Scopus)


Digital images are a main source of information in our modern digital era. However, the easiness of manipulating digital images using simple userfriendly software makes the credibility of images questionable. Copy-Move is one of the most common image forgery types, where a region of an image is copied and pasted into another location of the same image. Such a forgery is simple to achieve but hard to be detected as the pasted region shares the same characteristics with the image. Although plenty of algorithms have been proposed to tackle the copy-move detection problem, a fast and reliable copy-move detection algorithm is not achieved yet. In this paper, a new matching method is proposed which can reduce the detection time and enhance the accuracy of detection as well. Such enhancement is done by clustering image blocks into clusters, and searching for identical blocks within each cluster instead of all image blocks. For that purpose, k-means clustering is used to cluster the image blocks then Locality Sensitive Hashing (LSH) method is used to match the blocks based on Zernike moments. The experimental results shows that the processing time has been reduced to 10% and the detection accuracy has been enhanced as well.

Original languageEnglish
Title of host publicationInformation Science and Applications (ICISA) 2016
EditorsKuinam J. Kim, Nikolai Joukov
Place of PublicationSingapore Singapore
Number of pages10
ISBN (Electronic)9789811005572
ISBN (Print)9789811005565
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Information Science and Applications 2016 - Minh City, Vietnam
Duration: 15 Feb 201618 Feb 2016
Conference number: 7th

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInternational Conference on Information Science and Applications 2016
Abbreviated titleICISA 2016
CityMinh City
Internet address


  • Copy-move
  • Digital image forgery
  • K-means clustering
  • Locality sensitive hashing
  • Zernike moments

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