Photomontage detection using steganography technique based on a neural network.

Clicks: 152
ID: 17353
2019
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Ranked #45 of 55 articles by views in neural networks : the official journal of the international neural network society

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Abstract
This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN method uses the characteristics of neural networks to create suitable attributes which are then necessary for subsequent detection of modified photographs. This also results in a fact that if an image is signed by this technique, the detection of modifications does not need any external data (database of non-modified originals) and the quality of the signature in various parts of the image also serves to identify modified (corrupted) parts of the image. The experimental study was performed on photographs from CoMoFoD Database and its results were compared with other approaches using this database based on standard metrics. The performed study showed the ability of the StegoNN method to detect corrupted parts of an image and to mark places which have been most probably image-manipulated. The usage of this method is suitable for reportage photography, but in general, for all cases where verification (provability) of authenticity and veracity of the presented image are required.
Reference Key
jarusek2019photomontageneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jarusek, Robert;Volna, Eva;Kotyrba, Martin;
Journal neural networks : the official journal of the international neural network society
Year 2019
DOI
S0893-6080(19)30096-6
URL
Keywords Keywords not found

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