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A fusion framework based on fuzzy integrals for passive-blind image tamper detection

  • Mandeep Kaur
  • Savita Gupta
Article
  • 312 Downloads

Abstract

The innate complexity and uncertainty in the domain of image forensics has made the application of fusion technology an obligatory requirement. Fuzzy integrals, that provide a meaningful formalism for combining different information sources has gained limited consideration in image forensics. The current paper presents a fuzzy integral based fusion framework for image tamper detection that can exploit the interaction between multiple forensic tools for collaborative decision making. Four tools that expose traces of semantic manipulation based on specific statistical cues are designed that work cohesively to allow detection of forgeries in single image (copy-move), composite image (splicing) and two generic artefacts (double JPEG compression and noise inconsistency). The measurement level fusion of tool outcome is carried out with Sugneo and Choquet integrals as the underlying aggregation operators. The classification competency of each tool is evaluated on a specialized dataset of forged images with the respective forgery trace. The empirical evaluation of the fusion framework for blind tamper detection is carried out on a combined master dataset comprising the various forgery traces under study. The Choquet integral based aggregation exhibits an enhanced classification competency on comparison with other fusion approaches like Feature level, Fuzzy Logic based on if-else rules, Behaviour Knowledge Space, Dempster-Shafer combination and classifier ensemble architecture based on Wolpkart’s stacked generalization.

Keywords

Image forensics Tamper detection Information fusion Fuzzy-integral 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

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