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Cluster Computing
Published in: Cluster Computing 5/2019

04-12-2017

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

Authors: Mandeep Kaur, Savita Gupta

Published in: Cluster Computing | Special Issue 5/2019

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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.
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Metadata
Title
A fusion framework based on fuzzy integrals for passive-blind image tamper detection
Authors
Mandeep Kaur
Savita Gupta
Publication date
04-12-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1393-3

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