Cloning Localization Based on Feature Extraction and K-means Clustering

  • Areej S. Alfraih
  • Johann A. Briffa
  • Stephan Wesemeyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9023)

Abstract

The field of image forensics is expanding rapidly. Many passive image tamper detection techniques have been presented. Some of these techniques use feature extraction methods for tamper detection and localization. This work is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97 % using MSER features. The usability and efficacy of our approach is verified by comparing with recent state-of-the-art approaches.

Keywords

Cloning localization MSER features SIFT SURF K-means clustering 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Areej S. Alfraih
    • 1
  • Johann A. Briffa
    • 1
  • Stephan Wesemeyer
    • 1
  1. 1.Department of ComputingUniversity of SurreyGuildfordUK

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