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Unsupervised Image Manipulation Localization With Non-Binary Label Attribution | IEEE Journals & Magazine | IEEE Xplore

Unsupervised Image Manipulation Localization With Non-Binary Label Attribution


Abstract:

Existing forensic techniques for image manipulation localization crucially assume that probe pixels belong to one of exactly two classes, genuine or manipulated. This let...Show More

Abstract:

Existing forensic techniques for image manipulation localization crucially assume that probe pixels belong to one of exactly two classes, genuine or manipulated. This letter argues that this convention fuels mis-labeling particularly in unsupervised settings, where singular but genuine content or the presence of multiple distinct manipulations may easily induce non-optimal partitions of the feature space. We propose to relax constraints via a greedy n-ary clustering approach, which we instantiate exemplarily in the popular pixel descriptor space of residual co-occurrences. Experimental results on widely used public benchmark datasets highlight the benefits of our approach.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 7, July 2019)
Page(s): 976 - 980
Date of Publication: 26 April 2019

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